System

Abstract

An object of a system according to an embodiment is to efficiently detect malware on a server and quickly take measures.SOLUTION: A system according to an embodiment includes a scanning unit, a learning unit, and a countermeasure unit. The scan unit includes a generation AI. The learning unit learns characteristics of suspicious behavior and malware based on data of files and programs of the server scanned by the scanning unit. The countermeasure unit detects malware based on the feature learned by the learning unit, and takes a countermeasure.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to efficiently detect malware on servers and take prompt countermeasures.

[0005] The system according to the embodiment aims to efficiently detect malware on a server and take countermeasures promptly. [Means for solving the problem]

[0006] The system according to the embodiment includes a scanning unit, a learning unit, and a countermeasure unit. The scanning unit is equipped with a generative AI. The learning unit learns suspicious behavior and malware characteristics based on the file and program data of the server scanned by the scanning unit. The countermeasure unit detects malware based on the characteristics learned by the learning unit and takes countermeasures. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect malware on a server and take countermeasures promptly. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A malware guard system according to an embodiment of the present invention is a system that automatically detects and removes malware on a server using generative AI, thereby improving server security and minimizing damage caused by malware.

[0029] A malware guard system according to an embodiment includes a scanning unit, a learning unit, and a countermeasure unit. The scanning unit is equipped with a generative AI and scans files and programs on a server. For example, the scanning unit analyzes the file system in the server and checks the content and behavior of each file. The scanning unit can also perform periodic scans. For example, the scanning unit performs scans at a set time each day. The scanning unit can also perform scans in real time. For example, the scanning unit performs scans immediately when a file is changed. The learning unit learns suspicious behavior and malware characteristics based on the data on server files and programs scanned by the scanning unit. For example, the learning unit learns when a specific file is consuming an abnormally large amount of resources or when unexpected network communications are occurring. The learning unit can also extract more advanced characteristics by referring to past malware cases and attack patterns. For example, the learning unit learns the characteristics of new malware based on past attack data. The countermeasure unit detects malware based on the characteristics learned by the learning unit and takes countermeasures. For example, the countermeasure unit quarantines malware. The countermeasure unit can also delete malware. The countermeasure unit can also stop the operation of malware. For example, the countermeasure unit can forcibly terminate the malware process. This allows the malware guard system according to the embodiment to automatically detect and remove malware on the server. For example, the malware guard system scans files and programs on the server, learns suspicious behavior and malware characteristics, and immediately takes countermeasures against detected malware. This can improve server security and minimize damage caused by malware.

[0030] The scanning unit can analyze the file system within the server and check the content and behavior of each file. For example, the scanning unit can analyze the file system within the server and check the content and behavior of each file. For example, the scanning unit can check the header information and metadata of files. The scanning unit can also check the executable code of files. The scanning unit can also monitor file behavior and detect abnormal behavior. For example, the scanning unit can detect when a file is consuming an abnormally large number of resources or when unexpected network communication is occurring. This allows for more detailed scanning by analyzing the file system within the server and checking the content and behavior of each file.

[0031] The countermeasure unit can take measures to quarantine, delete, or stop the operation of malware. For example, the countermeasure unit quarantines detected malware. For example, the countermeasure unit moves the malware to a quarantine folder. The countermeasure unit can also delete detected malware. For example, the countermeasure unit completely deletes the malware file. The countermeasure unit can also stop the operation of detected malware. For example, the countermeasure unit forcibly terminates the malware process. In this way, by taking appropriate measures against detected malware, the safety of the server can be ensured.

[0032] The countermeasures department can prevent leaks of important data and system downtime. For example, the countermeasures department encrypts data to prevent leaks of important data. For example, the countermeasures department AES encrypts important data. The countermeasures department can also strengthen access control. For example, the countermeasures department restricts access to important data. The countermeasures department can also implement redundancy to prevent system downtime. For example, the countermeasures department regularly creates backups of the system. The countermeasures department can also execute failover. For example, the countermeasures department switches to another server when the system goes down. This can prevent leaks of important data and system downtime, improving server reliability.

[0033] The scanning unit can visualize in real time any abnormal file or program behavior detected during a scan and notify the administrator. The scanning unit, for example, visualizes in real time any abnormal file or program behavior detected during a scan. For example, the scanning unit displays files showing abnormal behavior in graphs or charts. The scanning unit can also notify the administrator of detected abnormalities. For example, the scanning unit notifies the administrator of the abnormality by email or SMS. The scanning unit can also notify the administrator of the abnormality by app notification. This makes it possible to visualize in real time any abnormal file or program behavior and notify the administrator, enabling a prompt response.

[0034] The scanning unit can expand the scanning target not only to files and programs in the server but also to network traffic and database queries. For example, the scanning unit can expand the scanning target not only to files and programs in the server but also to network traffic and database queries. For example, the scanning unit can detect abnormal network communications. The scanning unit can also detect abnormal database queries. In this way, expanding the scanning target enables a wider range of anomalies to be detected.

[0035] The learning unit can extract more advanced features by referring to past malware cases and attack patterns. The learning unit, for example, refers to past malware cases and attack patterns to extract more advanced features. For example, the learning unit learns the features of new malware based on past attack data. The learning unit can also refer to signature-based and behavior-based attack patterns. In this way, by referring to past malware cases and attack patterns, more advanced features can be extracted and detection accuracy can be improved.

[0036] When an abnormal behavior is detected, the learning unit can automatically analyze the cause and generate a report. For example, when an abnormal behavior is detected, the learning unit automatically analyzes the cause. For example, the learning unit performs log analysis and traceback. The learning unit can also perform correlation analysis. The learning unit can also generate a report based on the analysis results. For example, the learning unit identifies the cause of the abnormal behavior and generates a detailed report. In this way, by automatically analyzing the cause of the abnormal behavior and generating a report, the administrator can respond quickly.

[0037] The learning unit can expand the learning target not only to files and programs on the server but also to user operation logs and access histories. For example, the learning unit can expand the learning target not only to files and programs on the server but also to user operation logs and access histories. For example, the learning unit learns abnormal user operations and access patterns. The learning unit can also detect suspicious behavior based on operation logs and access histories. In this way, expanding the learning target enables a wider range of anomaly detection.

[0038] The learning unit can link the learning results with other security systems to realize comprehensive security measures. The learning unit, for example, links the learning results with other security systems. For example, the learning unit automatically updates firewall rules based on the learning results. The learning unit can also share the learning results with IDS / IPS. This allows the learning results to be linked with other security systems to realize comprehensive security measures.

[0039] The countermeasure unit can automatically analyze the extent of the impact of malware before taking countermeasures against it and select countermeasures that minimize the impact. The countermeasure unit, for example, automatically analyzes the extent of the impact of malware before taking countermeasures against it. For example, the countermeasure unit identifies the extent of the impact and evaluates the degree of impact. The countermeasure unit can also select countermeasures that minimize the impact. For example, the countermeasure unit automatically selects optimal countermeasures based on the extent of the impact. In this way, the stability of the system can be ensured by automatically analyzing the extent of the impact and selecting countermeasures that minimize the impact.

[0040] The countermeasures department can automatically generate a detailed analysis report of detected malware and provide it to an administrator. The countermeasures department, for example, automatically generates a detailed analysis report of detected malware. For example, the countermeasures department generates a report that details the behavior and scope of impact of the malware. The countermeasures department can also provide the generated report to an administrator. For example, the countermeasures department sends the report to the administrator by email or app notification. In this way, by automatically generating a detailed analysis report and providing it to the administrator, a prompt response is possible.

[0041] When the countermeasures department takes measures, it can automatically apply the same measures to other servers and cloud environments, thereby preventing the spread of malware. For example, when the countermeasures department takes measures, it can automatically apply the same measures to other servers and cloud environments. For example, the countermeasures department automatically deploys the measures to other servers. The countermeasures department can also apply measures to cloud environments. For example, the countermeasures department applies the measures to cloud environments such as AWS, Azure, and Google Cloud. This makes it possible to prevent the spread of malware by automatically applying the same measures to other servers and cloud environments when the countermeasures are taken.

[0042] The countermeasures department can share information about detected malware with other security systems, enabling comprehensive security measures. The countermeasures department, for example, shares information about detected malware with other security systems. For example, the countermeasures department shares malware information with firewalls and IDS / IPS. The countermeasures department can also share information through API integration. This allows information about detected malware to be shared with other security systems, enabling comprehensive security measures.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The malware guard system also includes a prediction unit. The prediction unit can predict the possibility of future malware attacks based on past scan data and learning data. For example, the prediction unit analyzes past attack patterns to identify the time periods when the next attack is likely to occur and server vulnerabilities. The prediction unit can also detect signs of attacks in real time based on abnormal network traffic and user behavior patterns. This allows the prediction unit to take countermeasures in advance and prevent damage from malware attacks.

[0045] The malware guard system also includes a notification unit. The notification unit can not only notify the administrator of information about detected malware and abnormal behavior, but also other relevant departments and external security experts. For example, when an abnormality is detected, the notification unit notifies the relevant departments via email or SMS. The notification unit can also send alerts to external security experts in real time to encourage a prompt response. This allows the notification unit to respond quickly and effectively when an abnormality is detected.

[0046] The malware guard system also includes an education department. The education department can provide security education to administrators and users based on information about detected malware and abnormal behavior. For example, the education department can create training materials that explain the characteristics of detected malware and countermeasures. The education department can also hold regular security seminars to share the latest security information. This allows the education department to improve the security awareness of administrators and users.

[0047] The malware guard system further includes a backup unit. The backup unit periodically backs up server data to prevent data loss due to malware attacks. For example, the backup unit backs up server data at a set time every day. The backup unit can also immediately perform a backup when an abnormality is detected. This allows the backup unit to minimize data loss due to malware attacks.

[0048] The malware guard system also includes an auditing department. The auditing department can periodically audit the security status of the entire system and propose improvements. For example, the auditing department can periodically analyze the system's security logs to identify potential vulnerabilities. The auditing department can also check compliance with security policies and propose improvements as necessary. This allows the auditing department to continuously improve the security of the entire system.

[0049] The malware guard system further includes a recovery unit. The recovery unit can quickly restore systems and data damaged by malware attacks. For example, the recovery unit can restore systems based on backup data. The recovery unit can also restore settings and files that have been changed by malware. This allows the recovery unit to minimize damage caused by malware attacks and quickly restore normal system operation.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The scanning unit is equipped with generative AI and scans the files and programs on the server. For example, the scanning unit analyzes the file system on the server and checks the contents and behavior of each file. The scanning unit can also perform scans periodically. For example, the scanning unit can perform scans at a set time every day. The scanning unit can also perform scans in real time. For example, the scanning unit can perform scans immediately when a file is changed. Step 2: The learning unit learns suspicious behavior and malware characteristics based on the server file and program data scanned by the scanning unit. For example, the learning unit learns when a specific file is consuming an abnormally large amount of resources or when unexpected network communications are occurring. The learning unit can also refer to past malware cases and attack patterns to extract more advanced characteristics. For example, the learning unit learns the characteristics of new malware based on past attack data. Step 3: The countermeasure unit detects malware based on the characteristics learned by the learning unit and takes countermeasures. For example, the countermeasure unit quarantines the malware. The countermeasure unit can also delete the malware. The countermeasure unit can also stop the operation of the malware. For example, the countermeasure unit can forcibly terminate the malware process.

[0052] (Example 2) A malware guard system according to an embodiment of the present invention is a system that automatically detects and removes malware on a server using generative AI, thereby improving server security and minimizing damage caused by malware.

[0053] A malware guard system according to an embodiment includes a scanning unit, a learning unit, and a countermeasure unit. The scanning unit is equipped with a generative AI and scans files and programs on a server. For example, the scanning unit analyzes the file system in the server and checks the content and behavior of each file. The scanning unit can also perform periodic scans. For example, the scanning unit performs scans at a set time each day. The scanning unit can also perform scans in real time. For example, the scanning unit performs scans immediately when a file is changed. The learning unit learns suspicious behavior and malware characteristics based on the data on server files and programs scanned by the scanning unit. For example, the learning unit learns when a specific file is consuming an abnormally large amount of resources or when unexpected network communications are occurring. The learning unit can also extract more advanced characteristics by referring to past malware cases and attack patterns. For example, the learning unit learns the characteristics of new malware based on past attack data. The countermeasure unit detects malware based on the characteristics learned by the learning unit and takes countermeasures. For example, the countermeasure unit quarantines malware. The countermeasure unit can also delete malware. The countermeasure unit can also stop the operation of malware. For example, the countermeasure unit can forcibly terminate the malware process. This allows the malware guard system according to the embodiment to automatically detect and remove malware on the server. For example, the malware guard system scans files and programs on the server, learns suspicious behavior and malware characteristics, and immediately takes countermeasures against detected malware. This can improve server security and minimize damage caused by malware.

[0054] The scanning unit can analyze the file system within the server and check the content and behavior of each file. For example, the scanning unit can analyze the file system within the server and check the content and behavior of each file. For example, the scanning unit can check the header information and metadata of files. The scanning unit can also check the executable code of files. The scanning unit can also monitor file behavior and detect abnormal behavior. For example, the scanning unit can detect when a file is consuming an abnormally large number of resources or when unexpected network communication is occurring. This allows for more detailed scanning by analyzing the file system within the server and checking the content and behavior of each file.

[0055] The countermeasure unit can take measures to quarantine, delete, or stop the operation of malware. For example, the countermeasure unit quarantines detected malware. For example, the countermeasure unit moves the malware to a quarantine folder. The countermeasure unit can also delete detected malware. For example, the countermeasure unit completely deletes the malware file. The countermeasure unit can also stop the operation of detected malware. For example, the countermeasure unit forcibly terminates the malware process. In this way, by taking appropriate measures against detected malware, the safety of the server can be ensured.

[0056] The countermeasures department can prevent leaks of important data and system downtime. For example, the countermeasures department encrypts data to prevent leaks of important data. For example, the countermeasures department AES encrypts important data. The countermeasures department can also strengthen access control. For example, the countermeasures department restricts access to important data. The countermeasures department can also implement redundancy to prevent system downtime. For example, the countermeasures department regularly creates backups of the system. The countermeasures department can also execute failover. For example, the countermeasures department switches to another server when the system goes down. This can prevent leaks of important data and system downtime, improving server reliability.

[0057] The scanning unit can visualize in real time any abnormal file or program behavior detected during a scan and notify the administrator. The scanning unit, for example, visualizes in real time any abnormal file or program behavior detected during a scan. For example, the scanning unit displays files showing abnormal behavior in graphs or charts. The scanning unit can also notify the administrator of detected abnormalities. For example, the scanning unit notifies the administrator of the abnormality by email or SMS. The scanning unit can also notify the administrator of the abnormality by app notification. This makes it possible to visualize in real time any abnormal file or program behavior and notify the administrator, enabling a prompt response.

[0058] The scanning unit can expand the scanning target not only to files and programs in the server but also to network traffic and database queries. For example, the scanning unit can expand the scanning target not only to files and programs in the server but also to network traffic and database queries. For example, the scanning unit can detect abnormal network communications. The scanning unit can also detect abnormal database queries. In this way, expanding the scanning target enables a wider range of anomalies to be detected.

[0059] The scanning unit can use the emotion estimation function to collect the user's emotions regarding anomalies detected during scanning in real time and optimize the scanning algorithm. The scanning unit, for example, uses the emotion estimation function to collect the user's emotions regarding anomalies detected during scanning in real time. For example, the scanning unit analyzes the user's facial expressions and voice to collect emotion data. The scanning unit can also optimize the scanning algorithm based on the collected emotion data. For example, the scanning unit adjusts the scanning priority based on the user's emotion data. This enables more effective scanning by optimizing the scanning algorithm based on the user's emotion.

[0060] The learning unit can extract more advanced features by referring to past malware cases and attack patterns. The learning unit, for example, refers to past malware cases and attack patterns to extract more advanced features. For example, the learning unit learns the features of new malware based on past attack data. The learning unit can also refer to signature-based and behavior-based attack patterns. In this way, by referring to past malware cases and attack patterns, more advanced features can be extracted and detection accuracy can be improved.

[0061] When an abnormal behavior is detected, the learning unit can automatically analyze the cause and generate a report. For example, when an abnormal behavior is detected, the learning unit automatically analyzes the cause. For example, the learning unit performs log analysis and traceback. The learning unit can also perform correlation analysis. The learning unit can also generate a report based on the analysis results. For example, the learning unit identifies the cause of the abnormal behavior and generates a detailed report. In this way, by automatically analyzing the cause of the abnormal behavior and generating a report, the administrator can respond quickly.

[0062] The learning unit can use the emotion estimation function to analyze the administrator's emotions regarding the learning results and dynamically adjust the parameters of the learning algorithm. The learning unit, for example, uses the emotion estimation function to analyze the administrator's emotions regarding the learning results. For example, the learning unit analyzes the administrator's facial expressions and voice to collect emotion data. The learning unit can also dynamically adjust the parameters of the learning algorithm based on the collected emotion data. For example, the learning unit optimizes hyperparameters based on the administrator's emotion data. In this way, learning accuracy can be improved by dynamically adjusting the parameters of the learning algorithm based on the administrator's emotions.

[0063] The learning unit can expand the learning target not only to files and programs on the server but also to user operation logs and access histories. For example, the learning unit can expand the learning target not only to files and programs on the server but also to user operation logs and access histories. For example, the learning unit learns abnormal user operations and access patterns. The learning unit can also detect suspicious behavior based on operation logs and access histories. In this way, expanding the learning target enables a wider range of anomaly detection.

[0064] The learning unit can link the learning results with other security systems to realize comprehensive security measures. The learning unit, for example, links the learning results with other security systems. For example, the learning unit automatically updates firewall rules based on the learning results. The learning unit can also share the learning results with IDS / IPS. This allows the learning results to be linked with other security systems to realize comprehensive security measures.

[0065] The learning unit can use the emotion estimation function to collect user emotions regarding anomalies detected during learning in real time and optimize the learning algorithm. The learning unit, for example, uses the emotion estimation function to collect user emotions regarding anomalies detected during learning in real time. For example, the learning unit analyzes the user's facial expressions and voice to collect emotion data. The learning unit can also optimize the learning algorithm based on the collected emotion data. For example, the learning unit adjusts the parameters of the learning algorithm based on the user's emotion data. This enables more effective learning by optimizing the learning algorithm based on the user's emotion.

[0066] The countermeasure unit can automatically analyze the extent of the impact of malware before taking countermeasures against it and select countermeasures that minimize the impact. The countermeasure unit, for example, automatically analyzes the extent of the impact of malware before taking countermeasures against it. For example, the countermeasure unit identifies the extent of the impact and evaluates the degree of impact. The countermeasure unit can also select countermeasures that minimize the impact. For example, the countermeasure unit automatically selects optimal countermeasures based on the extent of the impact. In this way, the stability of the system can be ensured by automatically analyzing the extent of the impact and selecting countermeasures that minimize the impact.

[0067] The countermeasures department can automatically generate a detailed analysis report of detected malware and provide it to an administrator. The countermeasures department, for example, automatically generates a detailed analysis report of detected malware. For example, the countermeasures department generates a report that details the behavior and scope of impact of the malware. The countermeasures department can also provide the generated report to an administrator. For example, the countermeasures department sends the report to the administrator by email or app notification. In this way, by automatically generating a detailed analysis report and providing it to the administrator, a prompt response is possible.

[0068] The countermeasure unit can use the emotion estimation function to analyze the administrator's emotions regarding the countermeasure results and dynamically adjust the parameters of the countermeasure algorithm. The countermeasure unit, for example, uses the emotion estimation function to analyze the administrator's emotions regarding the countermeasure results. For example, the countermeasure unit analyzes the administrator's facial expressions and voice to collect emotion data. The countermeasure unit can also dynamically adjust the parameters of the countermeasure algorithm based on the collected emotion data. For example, the countermeasure unit adjusts the parameters of the countermeasure algorithm based on the administrator's emotion data. In this way, the accuracy of the countermeasures can be improved by dynamically adjusting the parameters of the countermeasure algorithm based on the administrator's emotions.

[0069] When the countermeasures department takes measures, it can automatically apply the same measures to other servers and cloud environments, thereby preventing the spread of malware. For example, when the countermeasures department takes measures, it can automatically apply the same measures to other servers and cloud environments. For example, the countermeasures department automatically deploys the measures to other servers. The countermeasures department can also apply measures to cloud environments. For example, the countermeasures department applies the measures to cloud environments such as AWS, Azure, and Google Cloud. This makes it possible to prevent the spread of malware by automatically applying the same measures to other servers and cloud environments when the countermeasures are taken.

[0070] The countermeasures department can share information about detected malware with other security systems, enabling comprehensive security measures. The countermeasures department, for example, shares information about detected malware with other security systems. For example, the countermeasures department shares malware information with firewalls and IDS / IPS. The countermeasures department can also share information through API integration. This allows information about detected malware to be shared with other security systems, enabling comprehensive security measures.

[0071] The countermeasure unit can use the emotion estimation function to collect in real time the user's emotions regarding anomalies detected during countermeasures, and optimize the countermeasure algorithm. The countermeasure unit, for example, uses the emotion estimation function to collect in real time the user's emotions regarding anomalies detected during countermeasures. For example, the countermeasure unit analyzes the user's facial expressions and voice to collect emotion data. The countermeasure unit can also optimize the countermeasure algorithm based on the collected emotion data. For example, the countermeasure unit adjusts the parameters of the countermeasure algorithm based on the user's emotion data. This enables more effective countermeasures by optimizing the countermeasure algorithm based on the user's emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The malware guard system also includes a prediction unit. The prediction unit can predict the possibility of future malware attacks based on past scan data and learning data. For example, the prediction unit analyzes past attack patterns to identify the time periods when the next attack is likely to occur and server vulnerabilities. The prediction unit can also detect signs of attacks in real time based on abnormal network traffic and user behavior patterns. This allows the prediction unit to take countermeasures in advance and prevent damage from malware attacks.

[0074] The malware guard system also includes a notification unit. The notification unit can not only notify the administrator of information about detected malware and abnormal behavior, but also other relevant departments and external security experts. For example, when an abnormality is detected, the notification unit notifies the relevant departments via email or SMS. The notification unit can also send alerts to external security experts in real time to encourage a prompt response. This allows the notification unit to respond quickly and effectively when an abnormality is detected.

[0075] The countermeasures department can use the emotion estimation function to evaluate the user's satisfaction after implementing countermeasures and reflect the results in the next countermeasure. For example, the countermeasures department can analyze the user's facial expressions and voice after implementing countermeasures and collect satisfaction data. The countermeasures department can also evaluate the effectiveness of countermeasures based on the collected satisfaction data and optimize the next countermeasure algorithm. This allows the countermeasures department to improve the quality of countermeasures based on user satisfaction.

[0076] The malware guard system also includes an education department. The education department can provide security education to administrators and users based on information about detected malware and abnormal behavior. For example, the education department can create training materials that explain the characteristics of detected malware and countermeasures. The education department can also hold regular security seminars to share the latest security information. This allows the education department to improve the security awareness of administrators and users.

[0077] The scanning unit can use the emotion estimation function to collect the user's anxieties and concerns about the scan results in real time and adjust the way the scan results are displayed. For example, the scanning unit can analyze the user's facial expressions and voice to collect data on anxieties and concerns. The scanning unit can also adjust the way the scan results are displayed based on the collected data and provide the results in a format that is easy for the user to understand. This enables the scanning unit to provide scan results that take the user's emotions into consideration.

[0078] The malware guard system further includes a backup unit. The backup unit periodically backs up server data to prevent data loss due to malware attacks. For example, the backup unit backs up server data at a set time every day. The backup unit can also immediately perform a backup when an abnormality is detected. This allows the backup unit to minimize data loss due to malware attacks.

[0079] The learning unit can use the emotion estimation function to collect in real time the user's emotions regarding anomalies detected during learning and optimize the learning algorithm. For example, the learning unit analyzes the user's facial expressions and voice to collect emotion data. The learning unit can also adjust the parameters of the learning algorithm based on the collected emotion data. This allows the learning unit to optimize the learning algorithm based on the user's emotions, enabling more effective learning.

[0080] The malware guard system also includes an auditing department. The auditing department can periodically audit the security status of the entire system and propose improvements. For example, the auditing department can periodically analyze the system's security logs to identify potential vulnerabilities. The auditing department can also check compliance with security policies and propose improvements as necessary. This allows the auditing department to continuously improve the security of the entire system.

[0081] The countermeasure unit can use the emotion estimation function to collect in real time the user's emotions regarding anomalies detected during countermeasures and optimize the countermeasure algorithm. For example, the countermeasure unit analyzes the user's facial expressions and voice to collect emotion data. The countermeasure unit can also adjust the parameters of the countermeasure algorithm based on the collected emotion data. This allows the countermeasure unit to optimize the countermeasure algorithm based on the user's emotions, enabling more effective countermeasures.

[0082] The malware guard system further includes a recovery unit. The recovery unit can quickly restore systems and data damaged by malware attacks. For example, the recovery unit can restore systems based on backup data. The recovery unit can also restore settings and files that have been changed by malware. This allows the recovery unit to minimize damage caused by malware attacks and quickly restore normal system operation.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The scanning unit is equipped with generative AI and scans the files and programs on the server. For example, the scanning unit analyzes the file system on the server and checks the contents and behavior of each file. The scanning unit can also perform scans periodically. For example, the scanning unit can perform scans at a set time every day. The scanning unit can also perform scans in real time. For example, the scanning unit can perform scans immediately when a file is changed. Step 2: The learning unit learns suspicious behavior and malware characteristics based on the server file and program data scanned by the scanning unit. For example, the learning unit learns when a specific file is consuming an abnormally large amount of resources or when unexpected network communications are occurring. The learning unit can also refer to past malware cases and attack patterns to extract more advanced characteristics. For example, the learning unit learns the characteristics of new malware based on past attack data. Step 3: The countermeasure unit detects malware based on the characteristics learned by the learning unit and takes countermeasures. For example, the countermeasure unit quarantines the malware. The countermeasure unit can also delete the malware. The countermeasure unit can also stop the operation of the malware. For example, the countermeasure unit can forcibly terminate the malware process.

[0085] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a 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.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A scanning unit equipped with generation AI, a learning unit that learns suspicious behavior and malware characteristics based on the data of files and programs on the server scanned by the scanning unit; a countermeasure unit that detects the malware based on the features learned by the learning unit and takes measures against the malware. A system characterized by:

2. The scanning unit Analyze the file system in the server and check the contents and behavior of each file.

2. The system of claim 1.

3. The learning unit Refer to past malware cases and attack patterns to extract more advanced features.

2. The system of claim 1.

4. The countermeasure unit Before taking the countermeasure against the malware, the extent of the impact is automatically analyzed, and the countermeasure that minimizes the impact is selected.

2. The system of claim 1.

5. The scanning unit Collect real-time user feedback on anomalies detected during scanning to optimize scanning algorithms 2. The system of claim 1.

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