system

Abstract

To provide a system that efficiently creates a security software through simulation of attack and defense.SOLUTION: A system according to an embodiment comprises an attack simulation part, a defense simulation part, a result collection part, and a security creation part. The attack simulation part performs attack simulation. The defense simulation part simulates a defense measure on the basis of vulnerability discovered by the attack simulation part. The result collection part collects simulation results of the attack simulation part and the defense simulation part. The security creation part creates a security software on the basis of simulation results collected by the result collection part.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the development of security software is likely to fall into a cat-and-mouse game between hackers and the security side, and it is difficult to take efficient countermeasures.

[0005] The system according to the embodiment aims to efficiently create countermeasure software through attack and defense simulations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an attack simulation unit, a defense simulation unit, a results collection unit, and a countermeasure creation unit. The attack simulation unit performs attack simulations. The defense simulation unit simulates defense measures based on vulnerabilities discovered by the attack simulation unit. The results collection unit collects the simulation results from the attack simulation unit and the defense simulation unit. The countermeasure creation unit creates countermeasure software based on the simulation results collected by the results collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create countermeasures software through attack and defense simulations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The security system according to an embodiment of the present invention is a system that automatically develops security software by simulating a cat-and-mouse game between hackers and security teams using a generative AI. In this security system, the generative AI performs simulations on both the hacker and security sides. The hacker-side generative AI performs attack simulations incorporating the latest technologies and methods. For example, it discovers a new vulnerability and simulates an attack method that utilizes it. Next, the security-side generative AI simulates defensive measures against the hacker-side attack simulation. For example, it simulates creating a patch for the discovered vulnerability to prevent the attack. In this way, by having the hacker-side and security-side generative AIs perform simulations alternately, it is possible to automatically create security software that includes attacks that do not currently exist. This makes the development of security software more efficient and enables more advanced security measures. In this way, the AI ​​generating the hacker side and the security side alternately perform simulations, enabling the automatic creation of countermeasures software that includes attacks that do not currently exist. This streamlines the development of security software and enables more advanced security measures. As a result, the security system can automatically simulate the cat-and-mouse game between hackers and security teams, and efficiently create countermeasures software.

[0029] The security system according to this embodiment comprises an attack simulation unit, a defense simulation unit, a results collection unit, and a countermeasure creation unit. The attack simulation unit performs attack simulations using generative AI. The attack simulation unit performs attack simulations incorporating, for example, the latest technologies and methods. For example, the attack simulation unit can discover new vulnerabilities and simulate attack methods that utilize them. The attack simulation unit can also generate new attack methods by referring to past attack data. For example, the attack simulation unit can analyze past attack data and generate new attack methods based on similar patterns. The defense simulation unit performs defense simulations using generative AI. For example, the defense simulation unit simulates methods to prevent attacks by creating patches for discovered vulnerabilities. The defense simulation unit can also generate new defense methods by referring to past defense data. For example, the defense simulation unit can analyze past defense data and generate new defense methods based on similar patterns. The results collection unit collects the simulation results from the attack simulation unit and the defense simulation unit. The results collection unit can collect simulation results from, for example, the attack simulation unit and the defense simulation unit. The results collection unit can also optimize the collection method by referring to past simulation results. For example, the results collection unit can analyze past simulation results and optimize the collection method based on similar patterns. The countermeasure creation unit creates countermeasure software based on the collected simulation results. The countermeasure creation unit can also generate new countermeasure methods by referring to past countermeasure data. For example, the countermeasure creation unit can analyze past countermeasure data and generate new countermeasure methods based on similar patterns.As a result, the security system according to this embodiment can automatically simulate the cat-and-mouse game between hackers and security personnel, and efficiently create countermeasures software.

[0030] The attack simulation unit performs attack simulations using generative AI. Specifically, the generative AI incorporates the latest technologies and methods before conducting simulations. For example, the generative AI can refer to the latest vulnerability database and simulate attack methods that exploit newly discovered vulnerabilities. This ensures that the system is always able to respond to the latest attack methods. The generative AI can also analyze past attack data and generate new attack methods based on similar patterns. For example, it can use past attack data to predict how a particular attack pattern will evolve and create a new attack scenario based on that. Furthermore, the generative AI can generate complex attack scenarios that combine different attack methods. This allows the attack simulation unit to handle not only single attack methods but also sophisticated attacks that combine multiple methods. For example, it can simulate a scenario that combines phishing attacks and malware infections and evaluate their impact. This enables the attack simulation unit to provide more realistic and complex attack scenarios, thereby strengthening the overall security of the system.

[0031] The defense simulation unit performs defense simulations using generative AI. Specifically, the generative AI creates patches for discovered vulnerabilities and simulates their effectiveness. For example, the generative AI can automatically generate patches for specific vulnerabilities and verify through simulation whether those patches actually prevent attacks. This makes it possible to provide rapid and effective defense measures. The defense simulation unit can also analyze past defense data and generate new defense methods based on similar patterns. For example, it can use past defense data to identify the optimal defense method for a specific attack and propose new defense measures based on that. Furthermore, the defense simulation unit can generate complex defense scenarios that combine different defense methods. This allows it to support not only single defense methods but also advanced defense measures that combine multiple methods. For example, it can simulate the coordination of strengthened firewall settings and intrusion detection systems and evaluate their effectiveness. This allows the defense simulation unit to provide more realistic and complex defense scenarios, thereby strengthening the overall security of the system.

[0032] The results collection unit collects the simulation results from the attack simulation unit and the defense simulation unit. Specifically, the results collection unit centrally manages and stores detailed result data for each simulation for analysis. For example, as a result of attack simulations, it collects data such as which attack methods were successful, which vulnerabilities were exploited, the success rate of the attacks, and the scope of impact. Similarly, as a result of defense simulations, it collects data such as which defense methods were effective, which vulnerabilities were defended against, the success rate of the defenses, and the scope of impact. Furthermore, the results collection unit can optimize the collection method by referring to past simulation results. For example, by analyzing past simulation results and optimizing the collection method based on similar patterns, data can be collected more efficiently. This allows the results collection unit to improve the accuracy and reliability of the simulation results. The results collection unit can also share the collected data with other systems and departments. For example, the collected data can be stored on a cloud server and made accessible to the countermeasure creation unit and other security departments. This allows the results collection unit to provide important data for improving the security of the entire system.

[0033] The countermeasure creation unit creates countermeasure software based on the collected simulation results. Specifically, the unit analyzes data collected from the attack simulation unit and the defense simulation unit and automatically generates optimal countermeasure software. For example, it can create a patch for a specific vulnerability and generate countermeasure software that includes the procedure for applying that patch. The countermeasure creation unit can also generate new countermeasure methods by referring to past countermeasure data. For example, it can analyze past countermeasure data and propose new countermeasure methods based on similar patterns. Furthermore, the countermeasure creation unit can also generate complex countermeasure software that combines different countermeasure methods. This allows it to handle not only single countermeasure methods but also advanced countermeasures that combine multiple methods. For example, it can generate countermeasure software that includes strengthening firewall settings and linking with intrusion detection systems and evaluate its effectiveness. This allows the countermeasure creation unit to provide more realistic and complex countermeasure software, strengthening the security of the entire system. Furthermore, the countermeasure creation unit can automatically distribute the generated countermeasure software and apply it to the entire system. This makes it possible to implement security measures quickly and effectively.

[0034] The attack simulation unit can perform attack simulations incorporating the latest technologies and methods. For example, the attack simulation unit can discover new vulnerabilities and simulate attack methods that exploit them. Furthermore, the attack simulation unit can generate new attack methods by referencing past attack data. For example, the attack simulation unit can analyze past attack data and generate new attack methods based on similar patterns. This allows for more realistic attack simulations by incorporating the latest technologies and methods.

[0035] The defense simulation unit can create patches for discovered vulnerabilities and simulate methods to prevent attacks. For example, the defense simulation unit can create patches for discovered vulnerabilities and simulate methods to prevent attacks. Furthermore, the defense simulation unit can generate new defense methods by referring to past defense data. For example, the defense simulation unit can analyze past defense data and generate new defense methods based on similar patterns. This allows for effective prevention of attacks by creating patches for vulnerabilities.

[0036] The results collection unit can collect the simulation results from the attack simulation unit and the defense simulation unit. For example, the results collection unit can collect the simulation results from the attack simulation unit and the defense simulation unit. Furthermore, the results collection unit can optimize the collection method by referring to past simulation results. For example, the results collection unit can analyze past simulation results and optimize the collection method based on similar patterns. This allows for obtaining the information necessary for creating countermeasures software by collecting simulation results.

[0037] The countermeasure creation unit can create countermeasure software based on the collected simulation results. For example, the countermeasure creation unit can create countermeasure software based on the collected simulation results. Furthermore, the countermeasure creation unit can generate new countermeasure methods by referring to past countermeasure data. For example, the countermeasure creation unit can analyze past countermeasure data and generate new countermeasure methods based on similar patterns. This enables effective security measures by creating countermeasure software based on simulation results.

[0038] The attack simulation unit can generate new attack methods by referring to past attack data. For example, the attack simulation unit can analyze past attack data and generate new attack methods based on similar patterns. Furthermore, the attack simulation unit can identify unresolved vulnerabilities from past attack data and generate new attack methods that utilize them. It can also combine past attack data to generate new composite attack methods. This allows for more realistic attack simulations by generating new attack methods based on past attack data.

[0039] The attack simulation unit can perform simulations considering vulnerabilities in different industries and fields. For example, the attack simulation unit can consider vulnerabilities in the financial industry and perform phishing attack simulations. It can also consider vulnerabilities in the medical industry and perform ransomware attack simulations. Furthermore, it can consider vulnerabilities in the manufacturing industry and perform supply chain attack simulations. This allows for a wider variety of attack simulations by considering vulnerabilities in different industries and fields.

[0040] The attack simulation unit can perform simulations while considering geographical threat information. For example, the attack simulation unit can perform simulations while considering trends in cyberattacks occurring in a particular region. Furthermore, the attack simulation unit can adjust attack methods to consider vulnerabilities in geographically different regions. It can also generate attack scenarios for specific regions based on geographical threat information. This allows for more realistic attack simulations by considering geographical threat information.

[0041] The attack simulation unit can analyze trends on social media and simulate relevant attack methods. For example, the attack simulation unit can analyze trends on social media and simulate relevant attack methods. For example, the attack simulation unit can generate attack scenarios based on vulnerabilities that are trending on social media. The attack simulation unit can also analyze trends on social media and simulate relevant attack methods. Furthermore, the attack simulation unit can generate new attack methods based on information from social media. In this way, by analyzing trends on social media, it is possible to simulate the latest attack methods.

[0042] The defense simulation unit can generate new defense methods by referring to past defense data. For example, the defense simulation unit can analyze past defense data and generate new defense methods based on similar patterns. Furthermore, the defense simulation unit can identify unresolved vulnerabilities from past defense data and generate new defense methods utilizing them. It can also combine past defense data to generate new composite defense methods. This allows for more realistic defense simulations by generating new defense methods based on past defense data.

[0043] The defense simulation unit can perform simulations considering defense methods from different industries and fields. For example, the defense simulation unit can consider defense methods from the financial industry and perform defense simulations against phishing attacks. It can also consider defense methods from the medical industry and perform defense simulations against ransomware attacks. Furthermore, it can consider defense methods from the manufacturing industry and perform defense simulations against supply chain attacks. This allows for a wider variety of defense simulations by considering defense methods from different industries and fields.

[0044] The defense simulation unit can perform simulations while considering geographical threat information. For example, the defense simulation unit can perform simulations while considering trends in cyberattacks occurring in a specific region. Furthermore, the defense simulation unit can adjust defense methods to consider vulnerabilities in geographically different regions. It can also generate defense scenarios for specific regions based on geographical threat information. This allows for more realistic defense simulations by considering geographical threat information.

[0045] The defense simulation unit can analyze trends on social media and simulate relevant defense methods. For example, the defense simulation unit can analyze trends on social media and simulate relevant defense methods. For example, the defense simulation unit can generate defense scenarios based on vulnerabilities that are trending on social media. The defense simulation unit can also analyze trends on social media and simulate relevant defense methods. Furthermore, the defense simulation unit can generate new defense methods based on information from social media. In this way, by analyzing trends on social media, it is possible to simulate the latest defense methods.

[0046] The results collection unit can optimize the collection method by referring to past simulation results. For example, the results collection unit can analyze past simulation results and optimize the collection method based on similar patterns. Furthermore, the results collection unit can identify unresolved vulnerabilities from past simulation results and optimize the collection method based on them. The results collection unit can also combine past simulation results to optimize a new collection method. This allows for more efficient results collection by optimizing the collection method based on past simulation results.

[0047] The results collection unit can collect data while considering geographical threat information. For example, the results collection unit can collect data while considering trends in cyberattacks occurring in a specific region. Furthermore, the results collection unit can adjust its collection methods to consider vulnerabilities in geographically different regions. It can also collect simulation results for a specific region based on geographical threat information. This allows for more realistic results collection by considering geographical threat information.

[0048] The countermeasure creation unit can generate new countermeasures by referring to past countermeasure data. For example, the countermeasure creation unit can generate new countermeasures by referring to past countermeasure data. For example, the countermeasure creation unit can analyze past countermeasure data and generate new countermeasures based on similar patterns. In addition, the countermeasure creation unit can identify unresolved vulnerabilities from past countermeasure data and generate new countermeasures using them. Furthermore, the countermeasure creation unit can combine past countermeasure data to generate new composite countermeasures. As a result, by generating new countermeasures based on past countermeasure data, it becomes possible to create more effective countermeasure software.

[0049] The countermeasure development department can create countermeasures while considering geographical threat information. For example, the countermeasure development department can create countermeasures while considering geographical threat information. For example, the countermeasure development department can create countermeasures while considering trends in cyberattacks occurring in a particular region. Furthermore, the countermeasure development department can adjust countermeasure methods while considering vulnerabilities in geographically different regions. In addition, the countermeasure development department can create countermeasure software for a specific region based on geographical threat information. This makes it possible to create more realistic countermeasure software by considering geographical threat information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The security system may also include a behavioral analysis unit that analyzes user behavior patterns. For example, the behavioral analysis unit can analyze a user's past behavioral data to identify normal behavioral patterns. It can also detect abnormal behavior and issue warnings in real time. Furthermore, the behavioral analysis unit can adjust attack and defense simulation scenarios based on the user's behavioral patterns. This enables more effective security measures tailored to the user's behavioral patterns.

[0052] The security system can also be equipped with an anomaly detection unit. This unit can, for example, monitor network traffic and detect abnormal patterns. It can also analyze system logs to identify abnormal access or operations. Furthermore, the anomaly detection unit can automatically take countermeasures against detected anomalies. This enables real-time detection of anomalies and rapid response.

[0053] Security systems can also include a threat intelligence unit that collects threat information from different industries and sectors. For example, this unit could collect threat information from the financial industry to strengthen countermeasures against phishing attacks. It could also collect threat information from the healthcare industry to strengthen countermeasures against ransomware attacks. Furthermore, it could collect threat information from the manufacturing industry to strengthen countermeasures against supply chain attacks. This allows for a wider range of security measures by collecting threat information from different industries and sectors.

[0054] Security systems can also include a geographic information gathering unit to collect geographical threat information. This unit can, for example, collect trends in cyberattacks occurring in specific regions and implement countermeasures tailored to those regions. It can also collect vulnerabilities in geographically different regions and adjust countermeasures accordingly. Furthermore, it can create countermeasures for specific regions based on geographical threat information. This allows for more realistic security measures by collecting geographical threat information.

[0055] The security system can also include a trend analysis unit that analyzes trends on social media. For example, the trend analysis unit can generate attack scenarios based on vulnerabilities that are trending on social media. It can also analyze social media trends and simulate relevant defense methods. Furthermore, it can generate new attack and defense methods based on information from social media. This allows for the simulation of the latest attack and defense methods by analyzing social media trends.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The attack simulation unit performs attack simulations using generative AI. For example, it performs attack simulations incorporating the latest technologies and methods to discover new vulnerabilities and simulate attack methods that exploit them. It can also generate new attack methods by referring to past attack data. Step 2: The defense simulation unit performs defense simulations using generative AI. For example, it simulates how to create patches for discovered vulnerabilities and prevent attacks. It can also generate new defense methods by referring to past defense data. Step 3: The results collection unit collects the simulation results from the attack simulation unit and the defense simulation unit. For example, the collection method can be optimized by referring to past simulation results. Step 4: The countermeasure creation unit creates countermeasure software based on the collected simulation results. For example, it can also generate new countermeasure methods by referring to past countermeasure data.

[0058] (Example of form 2) The security system according to an embodiment of the present invention is a system that automatically develops security software by simulating a cat-and-mouse game between hackers and security teams using a generative AI. In this security system, the generative AI performs simulations on both the hacker and security sides. The hacker-side generative AI performs attack simulations incorporating the latest technologies and methods. For example, it discovers a new vulnerability and simulates an attack method that utilizes it. Next, the security-side generative AI simulates defensive measures against the hacker-side attack simulation. For example, it simulates creating a patch for the discovered vulnerability to prevent the attack. In this way, by having the hacker-side and security-side generative AIs perform simulations alternately, it is possible to automatically create security software that includes attacks that do not currently exist. This makes the development of security software more efficient and enables more advanced security measures. In this way, the AI ​​generating the hacker side and the security side alternately perform simulations, enabling the automatic creation of countermeasures software that includes attacks that do not currently exist. This streamlines the development of security software and enables more advanced security measures. As a result, the security system can automatically simulate the cat-and-mouse game between hackers and security teams, and efficiently create countermeasures software.

[0059] The security system according to this embodiment comprises an attack simulation unit, a defense simulation unit, a results collection unit, and a countermeasure creation unit. The attack simulation unit performs attack simulations using generative AI. The attack simulation unit performs attack simulations incorporating, for example, the latest technologies and methods. For example, the attack simulation unit can discover new vulnerabilities and simulate attack methods that utilize them. The attack simulation unit can also generate new attack methods by referring to past attack data. For example, the attack simulation unit can analyze past attack data and generate new attack methods based on similar patterns. The defense simulation unit performs defense simulations using generative AI. For example, the defense simulation unit simulates methods to prevent attacks by creating patches for discovered vulnerabilities. The defense simulation unit can also generate new defense methods by referring to past defense data. For example, the defense simulation unit can analyze past defense data and generate new defense methods based on similar patterns. The results collection unit collects the simulation results from the attack simulation unit and the defense simulation unit. The results collection unit can collect simulation results from, for example, the attack simulation unit and the defense simulation unit. The results collection unit can also optimize the collection method by referring to past simulation results. For example, the results collection unit can analyze past simulation results and optimize the collection method based on similar patterns. The countermeasure creation unit creates countermeasure software based on the collected simulation results. The countermeasure creation unit can also generate new countermeasure methods by referring to past countermeasure data. For example, the countermeasure creation unit can analyze past countermeasure data and generate new countermeasure methods based on similar patterns.As a result, the security system according to this embodiment can automatically simulate the cat-and-mouse game between hackers and security personnel, and efficiently create countermeasures software.

[0060] The attack simulation unit performs attack simulations using generative AI. Specifically, the generative AI incorporates the latest technologies and methods before conducting simulations. For example, the generative AI can refer to the latest vulnerability database and simulate attack methods that exploit newly discovered vulnerabilities. This ensures that the system is always able to respond to the latest attack methods. The generative AI can also analyze past attack data and generate new attack methods based on similar patterns. For example, it can use past attack data to predict how a particular attack pattern will evolve and create a new attack scenario based on that. Furthermore, the generative AI can generate complex attack scenarios that combine different attack methods. This allows the attack simulation unit to handle not only single attack methods but also sophisticated attacks that combine multiple methods. For example, it can simulate a scenario that combines phishing attacks and malware infections and evaluate their impact. This enables the attack simulation unit to provide more realistic and complex attack scenarios, thereby strengthening the overall security of the system.

[0061] The defense simulation unit performs defense simulations using generative AI. Specifically, the generative AI creates patches for discovered vulnerabilities and simulates their effectiveness. For example, the generative AI can automatically generate patches for specific vulnerabilities and verify through simulation whether those patches actually prevent attacks. This makes it possible to provide rapid and effective defense measures. The defense simulation unit can also analyze past defense data and generate new defense methods based on similar patterns. For example, it can use past defense data to identify the optimal defense method for a specific attack and propose new defense measures based on that. Furthermore, the defense simulation unit can generate complex defense scenarios that combine different defense methods. This allows it to support not only single defense methods but also advanced defense measures that combine multiple methods. For example, it can simulate the coordination of strengthened firewall settings and intrusion detection systems and evaluate their effectiveness. This allows the defense simulation unit to provide more realistic and complex defense scenarios, thereby strengthening the overall security of the system.

[0062] The results collection unit collects the simulation results from the attack simulation unit and the defense simulation unit. Specifically, the results collection unit centrally manages and stores detailed result data for each simulation for analysis. For example, as a result of attack simulations, it collects data such as which attack methods were successful, which vulnerabilities were exploited, the success rate of the attacks, and the scope of impact. Similarly, as a result of defense simulations, it collects data such as which defense methods were effective, which vulnerabilities were defended against, the success rate of the defenses, and the scope of impact. Furthermore, the results collection unit can optimize the collection method by referring to past simulation results. For example, by analyzing past simulation results and optimizing the collection method based on similar patterns, data can be collected more efficiently. This allows the results collection unit to improve the accuracy and reliability of the simulation results. The results collection unit can also share the collected data with other systems and departments. For example, the collected data can be stored on a cloud server and made accessible to the countermeasure creation unit and other security departments. This allows the results collection unit to provide important data for improving the security of the entire system.

[0063] The countermeasure creation unit creates countermeasure software based on the collected simulation results. Specifically, the unit analyzes data collected from the attack simulation unit and the defense simulation unit and automatically generates optimal countermeasure software. For example, it can create a patch for a specific vulnerability and generate countermeasure software that includes the procedure for applying that patch. The countermeasure creation unit can also generate new countermeasure methods by referring to past countermeasure data. For example, it can analyze past countermeasure data and propose new countermeasure methods based on similar patterns. Furthermore, the countermeasure creation unit can also generate complex countermeasure software that combines different countermeasure methods. This allows it to handle not only single countermeasure methods but also advanced countermeasures that combine multiple methods. For example, it can generate countermeasure software that includes strengthening firewall settings and linking with intrusion detection systems and evaluate its effectiveness. This allows the countermeasure creation unit to provide more realistic and complex countermeasure software, strengthening the security of the entire system. Furthermore, the countermeasure creation unit can automatically distribute the generated countermeasure software and apply it to the entire system. This makes it possible to implement security measures quickly and effectively.

[0064] The attack simulation unit can perform attack simulations incorporating the latest technologies and methods. For example, the attack simulation unit can discover new vulnerabilities and simulate attack methods that exploit them. Furthermore, the attack simulation unit can generate new attack methods by referencing past attack data. For example, the attack simulation unit can analyze past attack data and generate new attack methods based on similar patterns. This allows for more realistic attack simulations by incorporating the latest technologies and methods.

[0065] The defense simulation unit can create patches for discovered vulnerabilities and simulate methods to prevent attacks. For example, the defense simulation unit can create patches for discovered vulnerabilities and simulate methods to prevent attacks. Furthermore, the defense simulation unit can generate new defense methods by referring to past defense data. For example, the defense simulation unit can analyze past defense data and generate new defense methods based on similar patterns. This allows for effective prevention of attacks by creating patches for vulnerabilities.

[0066] The results collection unit can collect the simulation results from the attack simulation unit and the defense simulation unit. For example, the results collection unit can collect the simulation results from the attack simulation unit and the defense simulation unit. Furthermore, the results collection unit can optimize the collection method by referring to past simulation results. For example, the results collection unit can analyze past simulation results and optimize the collection method based on similar patterns. This allows for obtaining the information necessary for creating countermeasures software by collecting simulation results.

[0067] The countermeasure creation unit can create countermeasure software based on the collected simulation results. For example, the countermeasure creation unit can create countermeasure software based on the collected simulation results. Furthermore, the countermeasure creation unit can generate new countermeasure methods by referring to past countermeasure data. For example, the countermeasure creation unit can analyze past countermeasure data and generate new countermeasure methods based on similar patterns. This enables effective security measures by creating countermeasure software based on simulation results.

[0068] The attack simulation unit can estimate the user's emotions and adjust the attack simulation scenario based on the estimated emotions. For example, if the user is feeling anxious, the attack simulation unit can lower the difficulty of the simulation and construct a scenario centered on basic attack methods. If the user is confident, the attack simulation unit can also increase the difficulty of the simulation and construct a scenario that includes complex attack methods. Furthermore, if the user is excited, the attack simulation unit can speed up the simulation and construct a scenario that includes rapid attack methods. By adjusting the simulation scenario according to the user's emotions, a more appropriate simulation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The attack simulation unit can generate new attack methods by referring to past attack data. For example, the attack simulation unit can analyze past attack data and generate new attack methods based on similar patterns. Furthermore, the attack simulation unit can identify unresolved vulnerabilities from past attack data and generate new attack methods that utilize them. It can also combine past attack data to generate new composite attack methods. This allows for more realistic attack simulations by generating new attack methods based on past attack data.

[0070] The attack simulation unit can perform simulations considering vulnerabilities in different industries and fields. For example, the attack simulation unit can consider vulnerabilities in the financial industry and perform phishing attack simulations. It can also consider vulnerabilities in the medical industry and perform ransomware attack simulations. Furthermore, it can consider vulnerabilities in the manufacturing industry and perform supply chain attack simulations. This allows for a wider variety of attack simulations by considering vulnerabilities in different industries and fields.

[0071] The attack simulation unit can estimate the user's emotions and determine the priority of attack simulations based on the estimated emotions. For example, if the user is feeling anxious, the attack simulation unit will prioritize simulating basic attack methods. If the user is confident, the attack simulation unit can also prioritize simulating complex attack methods. If the user is excited, the attack simulation unit can also prioritize simulating rapid attack methods. By determining the priority of simulations according to the user's emotions, more appropriate simulations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The attack simulation unit can perform simulations while considering geographical threat information. For example, the attack simulation unit can perform simulations while considering trends in cyberattacks occurring in a particular region. Furthermore, the attack simulation unit can adjust attack methods to consider vulnerabilities in geographically different regions. It can also generate attack scenarios for specific regions based on geographical threat information. This allows for more realistic attack simulations by considering geographical threat information.

[0073] The attack simulation unit can analyze trends on social media and simulate relevant attack methods. For example, the attack simulation unit can analyze trends on social media and simulate relevant attack methods. For example, the attack simulation unit can generate attack scenarios based on vulnerabilities that are trending on social media. The attack simulation unit can also analyze trends on social media and simulate relevant attack methods. Furthermore, the attack simulation unit can generate new attack methods based on information from social media. In this way, by analyzing trends on social media, it is possible to simulate the latest attack methods.

[0074] The defense simulation unit can estimate the user's emotions and adjust the defense simulation scenario based on the estimated emotions. For example, if the user is feeling anxious, the defense simulation unit will construct a scenario centered on basic defense techniques. If the user is confident, the defense simulation unit can also construct a scenario that includes complex defense techniques. If the user is excited, the defense simulation unit can also construct a scenario that includes rapid defense techniques. By adjusting the simulation scenario according to the user's emotions, a more appropriate defense simulation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The defense simulation unit can generate new defense methods by referring to past defense data. For example, the defense simulation unit can analyze past defense data and generate new defense methods based on similar patterns. Furthermore, the defense simulation unit can identify unresolved vulnerabilities from past defense data and generate new defense methods utilizing them. It can also combine past defense data to generate new composite defense methods. This allows for more realistic defense simulations by generating new defense methods based on past defense data.

[0076] The defense simulation unit can perform simulations considering defense methods from different industries and fields. For example, the defense simulation unit can consider defense methods from the financial industry and perform defense simulations against phishing attacks. It can also consider defense methods from the medical industry and perform defense simulations against ransomware attacks. Furthermore, it can consider defense methods from the manufacturing industry and perform defense simulations against supply chain attacks. This allows for a wider variety of defense simulations by considering defense methods from different industries and fields.

[0077] The defense simulation unit can estimate the user's emotions and determine the priority of defense simulations based on the estimated emotions. For example, if the user is feeling anxious, the defense simulation unit will prioritize simulating basic defense methods. If the user is confident, the defense simulation unit can also prioritize simulating complex defense methods. If the user is excited, the defense simulation unit can also prioritize simulating rapid defense methods. By determining the priority of simulations according to the user's emotions, more appropriate defense simulations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The defense simulation unit can perform simulations while considering geographical threat information. For example, the defense simulation unit can perform simulations while considering trends in cyberattacks occurring in a specific region. Furthermore, the defense simulation unit can adjust defense methods to consider vulnerabilities in geographically different regions. It can also generate defense scenarios for specific regions based on geographical threat information. This allows for more realistic defense simulations by considering geographical threat information.

[0079] The defense simulation unit can analyze trends on social media and simulate relevant defense methods. For example, the defense simulation unit can analyze trends on social media and simulate relevant defense methods. For example, the defense simulation unit can generate defense scenarios based on vulnerabilities that are trending on social media. The defense simulation unit can also analyze trends on social media and simulate relevant defense methods. Furthermore, the defense simulation unit can generate new defense methods based on information from social media. In this way, by analyzing trends on social media, it is possible to simulate the latest defense methods.

[0080] The results collection unit can estimate the user's emotions and determine the priority of simulation results to collect based on the estimated user emotions. For example, if the user is feeling anxious, the results collection unit will prioritize collecting basic simulation results. If the user is feeling confident, the results collection unit may also prioritize collecting complex simulation results. If the user is excited, the results collection unit may also prioritize collecting rapid simulation results. This allows for more appropriate result collection by prioritizing simulation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The results collection unit can optimize the collection method by referring to past simulation results. For example, the results collection unit can analyze past simulation results and optimize the collection method based on similar patterns. Furthermore, the results collection unit can identify unresolved vulnerabilities from past simulation results and optimize the collection method based on them. The results collection unit can also combine past simulation results to optimize a new collection method. This allows for more efficient results collection by optimizing the collection method based on past simulation results.

[0082] The results collection unit can estimate the user's emotions and adjust the display method of the collected simulation results based on the estimated user emotions. For example, if the user is feeling anxious, the results collection unit can provide a simple and highly visible display method. If the user is feeling confident, the results collection unit can also provide a display method that includes detailed information. If the user is excited, the results collection unit can also provide a visually stimulating display method. By adjusting the display method according to the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The results collection unit can collect data while considering geographical threat information. For example, the results collection unit can collect data while considering trends in cyberattacks occurring in a specific region. Furthermore, the results collection unit can adjust its collection methods to consider vulnerabilities in geographically different regions. It can also collect simulation results for a specific region based on geographical threat information. This allows for more realistic results collection by considering geographical threat information.

[0084] The countermeasure creation unit can estimate the user's emotions and adjust the method of creating the countermeasure software based on the estimated user emotions. For example, if the user is feeling anxious, the countermeasure creation unit will create countermeasure software focusing on basic countermeasures. If the user is confident, the countermeasure creation unit can also create countermeasure software that includes complex countermeasures. If the user is excited, the countermeasure creation unit can also create countermeasure software that includes rapid countermeasures. By adjusting the creation method according to the user's emotions, it becomes possible to create more appropriate countermeasure software. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The countermeasure creation unit can generate new countermeasures by referring to past countermeasure data. For example, the countermeasure creation unit can generate new countermeasures by referring to past countermeasure data. For example, the countermeasure creation unit can analyze past countermeasure data and generate new countermeasures based on similar patterns. In addition, the countermeasure creation unit can identify unresolved vulnerabilities from past countermeasure data and generate new countermeasures using them. Furthermore, the countermeasure creation unit can combine past countermeasure data to generate new composite countermeasures. As a result, by generating new countermeasures based on past countermeasure data, it becomes possible to create more effective countermeasure software.

[0086] The countermeasure creation unit can estimate the user's emotions and determine the priority of countermeasure software based on the estimated user emotions. For example, if the user is feeling anxious, the countermeasure creation unit will prioritize basic countermeasures when creating the countermeasure software. If the user is confident, the countermeasure creation unit can also prioritize complex countermeasures when creating the countermeasure software. If the user is excited, the countermeasure creation unit can also prioritize rapid countermeasures when creating the countermeasure software. By determining priorities according to the user's emotions, it becomes possible to create more appropriate countermeasure software. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The countermeasure development department can create countermeasures while considering geographical threat information. For example, the countermeasure development department can create countermeasures while considering geographical threat information. For example, the countermeasure development department can create countermeasures while considering trends in cyberattacks occurring in a particular region. Furthermore, the countermeasure development department can adjust countermeasure methods while considering vulnerabilities in geographically different regions. In addition, the countermeasure development department can create countermeasure software for a specific region based on geographical threat information. This makes it possible to create more realistic countermeasure software by considering geographical threat information.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The security system may also include a behavioral analysis unit that analyzes user behavior patterns. For example, the behavioral analysis unit can analyze a user's past behavioral data to identify normal behavioral patterns. It can also detect abnormal behavior and issue warnings in real time. Furthermore, the behavioral analysis unit can adjust attack and defense simulation scenarios based on the user's behavioral patterns. This enables more effective security measures tailored to the user's behavioral patterns.

[0090] The security system can also be equipped with an anomaly detection unit. This unit can, for example, monitor network traffic and detect abnormal patterns. It can also analyze system logs to identify abnormal access or operations. Furthermore, the anomaly detection unit can automatically take countermeasures against detected anomalies. This enables real-time detection of anomalies and rapid response.

[0091] The security system may also include an interface adjustment unit that estimates the user's emotions and adjusts the system's interface based on those emotions. For example, if the user is feeling anxious, the interface adjustment unit might provide a simple and intuitive interface. If the user is confident, it could provide an interface with more detailed information. Furthermore, if the user is excited, it could provide a visually stimulating interface. This allows for a more comfortable user experience by providing an interface that responds to the user's emotions.

[0092] The security system may also include a notification adjustment unit that estimates the user's emotions and adjusts the notification method based on those emotions. For example, if the user is feeling anxious, the notification adjustment unit may provide a simple and clear notification. If the user is feeling confident, it may provide a notification containing more detailed information. Furthermore, if the user is excited, it may provide a visually stimulating notification. This allows for more effective information transmission by providing notification methods that are tailored to the user's emotions.

[0093] The security system may also include a training adjustment unit that estimates the user's emotions and adjusts the training program based on those emotions. For example, if the user is feeling anxious, the training adjustment unit might provide a basic training program. If the user is confident, it could provide a more complex training program. Furthermore, if the user is excited, it could provide a rapid training program. This allows for more effective learning by providing training programs tailored to the user's emotions.

[0094] Security systems can also include a threat intelligence unit that collects threat information from different industries and sectors. For example, this unit could collect threat information from the financial industry to strengthen countermeasures against phishing attacks. It could also collect threat information from the healthcare industry to strengthen countermeasures against ransomware attacks. Furthermore, it could collect threat information from the manufacturing industry to strengthen countermeasures against supply chain attacks. This allows for a wider range of security measures by collecting threat information from different industries and sectors.

[0095] Security systems can also include a geographic information gathering unit to collect geographical threat information. This unit can, for example, collect trends in cyberattacks occurring in specific regions and implement countermeasures tailored to those regions. It can also collect vulnerabilities in geographically different regions and adjust countermeasures accordingly. Furthermore, it can create countermeasures for specific regions based on geographical threat information. This allows for more realistic security measures by collecting geographical threat information.

[0096] The security system can also include a trend analysis unit that analyzes trends on social media. For example, the trend analysis unit can generate attack scenarios based on vulnerabilities that are trending on social media. It can also analyze social media trends and simulate relevant defense methods. Furthermore, it can generate new attack and defense methods based on information from social media. This allows for the simulation of the latest attack and defense methods by analyzing social media trends.

[0097] The security system may also include an operation adjustment unit that estimates the user's emotions and adjusts the system's behavior based on those emotions. For example, if the user is feeling anxious, the operation adjustment unit may stabilize the system's behavior and provide predictable actions. If the user is confident, it may also make the system's behavior more flexible and allow for more complex operations. Furthermore, if the user is excited, it may make the system's behavior faster and provide immediate feedback. By providing system behavior that responds to the user's emotions, a more comfortable user experience can be achieved.

[0098] The security system may also include a performance adjustment unit that estimates the user's emotions and adjusts the system's performance based on those emotions. For example, if the user is feeling anxious, the performance adjustment unit can stabilize the system's performance and provide predictable behavior. If the user is confident, it can improve system performance and enable complex operations. Furthermore, if the user is excited, it can speed up system performance and provide immediate feedback. This allows for a more comfortable user experience by providing system performance that responds to the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The attack simulation unit performs attack simulations using generative AI. For example, it performs attack simulations incorporating the latest technologies and methods to discover new vulnerabilities and simulate attack methods that exploit them. It can also generate new attack methods by referring to past attack data. Step 2: The defense simulation unit performs defense simulations using generative AI. For example, it simulates how to create patches for discovered vulnerabilities and prevent attacks. It can also generate new defense methods by referring to past defense data. Step 3: The results collection unit collects the simulation results from the attack simulation unit and the defense simulation unit. For example, the collection method can be optimized by referring to past simulation results. Step 4: The countermeasure creation unit creates countermeasure software based on the collected simulation results. For example, it can also generate new countermeasure methods by referring to past countermeasure data.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the attack simulation unit, defense simulation unit, results collection unit, and countermeasure creation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the attack simulation unit is implemented by the processor 46 of the smart device 14 and performs attack simulations incorporating the latest technologies and methods. The defense simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates methods to create patches for discovered vulnerabilities and prevent attacks. The results collection unit is implemented by the control unit 46A of the smart device 14 and collects the simulation results from the attack simulation unit and the defense simulation unit. The countermeasure creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates countermeasure software based on the collected simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the attack simulation unit, defense simulation unit, results collection unit, and countermeasure creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the attack simulation unit is implemented by the processor 46 of the smart glasses 214 and performs attack simulations incorporating the latest technologies and methods. The defense simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates methods to create patches for discovered vulnerabilities and prevent attacks. The results collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the simulation results from the attack simulation unit and the defense simulation unit. The countermeasure creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates countermeasure software based on the collected simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the attack simulation unit, defense simulation unit, results collection unit, and countermeasure creation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the attack simulation unit is implemented by the processor 46 of the headset terminal 314 and performs attack simulations incorporating the latest technologies and methods. The defense simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates methods to create patches for discovered vulnerabilities and prevent attacks. The results collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the simulation results from the attack simulation unit and the defense simulation unit. The countermeasure creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates countermeasure software based on the collected simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the attack simulation unit, defense simulation unit, results collection unit, and countermeasure creation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the attack simulation unit is implemented by the processor 46 of the robot 414 and performs attack simulations incorporating the latest technologies and methods. The defense simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates methods to create patches for discovered vulnerabilities and prevent attacks. The results collection unit is implemented by the control unit 46A of the robot 414 and collects the simulation results from the attack simulation unit and the defense simulation unit. The countermeasure creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates countermeasure software based on the collected simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) The attack simulation department conducts attack simulations, A defense simulation unit simulates defense measures based on vulnerabilities discovered by the attack simulation unit, A result collection unit that collects the simulation results from the attack simulation unit and the defense simulation unit, The system includes a countermeasure creation unit that creates countermeasure software based on the simulation results collected by the results collection unit. A system characterized by the following features. (Note 2) The aforementioned attack simulation unit, We conduct attack simulations by specifically incorporating the latest technologies and methods. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned defense simulation unit, Create patches for discovered vulnerabilities and simulate how to prevent attacks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned results collection unit, The simulation results from the attack simulation unit and the defense simulation unit are collected. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned countermeasure creation unit, Create countermeasures software based on the collected simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned attack simulation unit, It estimates user emotions and adjusts attack simulation scenarios based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned attack simulation unit, New attack methods are generated by referencing past attack data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned attack simulation unit, The simulation will take into account the vulnerabilities of specific industries and fields. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned attack simulation unit, It estimates user sentiment and determines the priority of attack simulations based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned attack simulation unit, The simulation will take geographical threat information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned attack simulation unit, Analyze social media trends and simulate related attack methods. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned defense simulation unit, It estimates the user's emotions and adjusts the defense simulation scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned defense simulation unit, Generate new defense methods by referring to past defense data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned defense simulation unit, The simulation takes into account defense methods from different industries and fields. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned defense simulation unit, The system estimates user emotions and determines the priority of defense simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned defense simulation unit, The simulation will take geographical threat information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned defense simulation unit, Analyze social media trends and simulate relevant defense strategies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned results collection unit, It estimates user emotions and determines the priority of simulation results to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned results collection unit, We will specifically improve the data collection method by referring to past simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned results collection unit, We estimate the user's emotions and adjust how the simulation results collected based on those estimated emotions are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned results collection unit, Gathering information while considering geographical threat intelligence. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned countermeasure creation unit, The system estimates the user's emotions and adjusts the method of creating the countermeasures software based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned countermeasure creation unit, Generate new countermeasures by referring to past countermeasure data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned countermeasure creation unit, It estimates the user's emotions and determines the priority of security software based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned countermeasure creation unit, Develop countermeasures that take geographical threat information into account. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The attack simulation department conducts attack simulations, A defense simulation unit simulates defense measures based on vulnerabilities discovered by the attack simulation unit, A result collection unit that collects the simulation results of the attack simulation unit and the defense simulation unit, The system includes a countermeasure creation unit that creates countermeasure software based on the simulation results collected by the results collection unit, The attack simulation unit estimates the user's emotions and adjusts the attack simulation scenario so that if the estimated user's emotions indicate anxiety, it lowers the difficulty of the attack simulation and constructs a scenario centered on basic attack methods, and if the estimated user's emotions indicate confidence, it increases the difficulty of the attack simulation and constructs a scenario that includes complex attack methods. A system characterized by the following features.

2. The aforementioned defense simulation unit, Create patches for discovered vulnerabilities and simulate how to prevent attacks. The system according to feature 1.

3. The aforementioned attack simulation unit, If the estimated user's emotions are excited, the attack simulation scenario is adjusted to speed up the simulation and include a scenario with rapid attack methods. The system according to feature 1.

4. The aforementioned attack simulation unit, New attack methods are generated by referencing past attack data. The system according to feature 1.

5. The aforementioned countermeasure creation unit, Develop countermeasures that take geographical threat information into account. The system according to feature 1.

6. The aforementioned defense simulation unit, Analyze social media trends and simulate defensive measures, including relevant defensive techniques. The system according to feature 1.

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