Network data dynamic encryption updating method, system and device and storage medium

Abstract

The invention discloses a network data dynamic encryption updating method, system and device and a storage medium, and relates to the technical field of network security, and the method comprises the steps: generating an encryption key in a mode of combining a quantum random number generator and a machine learning algorithm; monitoring and analyzing an application scene of the original network data in real time to obtain a scene analysis result; evaluating the encryption algorithm in real time to obtain an algorithm evaluation result; selecting an encryption algorithm according to the scene analysis result and the algorithm evaluation result to obtain a target encryption algorithm; encrypting the original network data based on the encryption key to obtain encrypted network data; monitoring security threats in the network in real time to obtain alarm information; determining whether to update an encryption mode according to the alarm information; and continuing to encrypt the original network data under the condition of determining to update the encryption mode. Therefore, the security of the data is improved.

Description

Technical Field

[0001] This application relates to the field of network security technology, specifically to a method, system, device, and storage medium for dynamic encrypted updating of network data. Background Technology

[0002] With the rapid development of information technology, the secure transmission and storage of network data has become crucial. In the current network environment, data faces numerous security threats, such as hacker attacks, data breaches, and malicious tampering. Traditional encryption methods have limitations in addressing these threats. For example, once a static encryption key is cracked, the entire encryption system will collapse, and data security cannot be guaranteed; fixed encryption algorithms may not be able to adapt to the diverse needs for encryption strength and efficiency in different scenarios. Summary of the Invention

[0003] In view of this, this application provides a method, system, device and storage medium for dynamic encrypted updating of network data to improve data security.

[0004] The objective of this application can be achieved through the following technical solutions: The first aspect of this application is to provide a method for dynamically encrypting and updating network data, including: The encryption key is generated by combining a quantum random number generator with a machine learning algorithm. Real-time monitoring and analysis of the application scenarios of raw network data are performed to obtain scenario analysis results; The encryption algorithm is evaluated in real time to obtain the algorithm evaluation result; Based on the scenario analysis results and algorithm evaluation results, an encryption algorithm is selected to obtain the target encryption algorithm; The original network data is encrypted using the target encryption algorithm and encryption key to obtain the encrypted network data. Real-time monitoring of network security threats and receipt of alerts; Determine whether to update the encryption method based on the alarm information; If the encryption method is determined to be updated, the step of generating an encryption key using a combination of a quantum random number generator and a machine learning algorithm continues to encrypt the original network data.

[0005] In an optional embodiment, after generating the encryption key using a combination of a quantum random number generator and a machine learning algorithm, the method further includes: Utilize blockchain technology to store encryption keys; Encryption keys are distributed based on secure multi-party computation technology.

[0006] In one optional embodiment, the encryption key is generated by combining a quantum random number generator and a machine learning algorithm, including: The encryption key is generated using the following formula, which combines a quantum random number generator and a machine learning algorithm: ; in, This represents the encryption key. Random numbers generated by a quantum random number generator. For machine learning algorithms based on network parameters The processing results obtained, This is a combination function.

[0007] In one alternative embodiment, the encryption key is stored using blockchain technology, including: The encryption key is divided into multiple fragments, each fragment is stored on a different blockchain node, and each fragment is encrypted.

[0008] In an alternative embodiment, after dividing the encryption key into multiple fragments, the method further includes: Generate a unique identifier and access permissions for each fragment.

[0009] In an optional embodiment, before encrypting the original network data according to the target encryption algorithm and encryption key, the method further includes: The raw network data is preprocessed to obtain preprocessed network data. The preprocessing operation includes at least one of the following operations: cleaning, format conversion and compression. Encrypting the original network data according to the target encryption algorithm and encryption key, including: The preprocessed network data is encrypted using the target encryption algorithm and encryption key.

[0010] In one optional embodiment, after encrypting the original network data according to the target encryption algorithm and encryption key to obtain the encrypted network data, the method further includes: Post-processing operations are performed on encrypted network data to obtain target data. The post-processing operations include at least one of the following: adding encryption identifiers, digital signatures, and integrity verification.

[0011] A second aspect of this application is to provide a network data dynamic encryption update system, comprising: The first generation module is used to generate encryption keys by combining a quantum random number generator and a machine learning algorithm. The analysis module is used to monitor and analyze the application scenarios of raw network data in real time and obtain scenario analysis results; The evaluation module is used to evaluate the encryption algorithm in real time and obtain the algorithm evaluation results; The selection module is used to select an encryption algorithm based on the scenario analysis results and algorithm evaluation results, thereby obtaining the target encryption algorithm. The encryption module is used to encrypt the original network data according to the target encryption algorithm and encryption key to obtain the encrypted network data; The monitoring module is used to monitor security threats in the network in real time and obtain alarm information; The determination module is used to determine whether to update the encryption method based on the alarm information. The second generation module is used to continue executing the step of generating an encryption key by combining a quantum random number generator and a machine learning algorithm, after determining the updated encryption method, to encrypt the original network data.

[0012] A third aspect of this application is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the method as described in the first aspect.

[0013] A fourth aspect of this application is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the method as described in the first aspect.

[0014] Compared with existing technologies, the network data dynamic encryption update method provided in this application generates encryption keys by combining a quantum random number generator and a machine learning algorithm; it performs real-time monitoring and analysis of the application scenario of the original network data to obtain scenario analysis results; it evaluates the encryption algorithm in real-time to obtain algorithm evaluation results; it selects an encryption algorithm based on the scenario analysis results and algorithm evaluation results to obtain a target encryption algorithm; it encrypts the original network data using the target encryption algorithm and the encryption key to obtain encrypted network data; it monitors security threats in the network in real-time to obtain alarm information; it determines whether to update the encryption method based on the alarm information; if it determines to update the encryption method, it continues to execute the step of generating encryption keys by combining a quantum random number generator and a machine learning algorithm to encrypt the original network data. By dynamically generating encryption keys and combining a quantum random number generator and a machine learning algorithm, the keys have extremely high randomness and security, effectively preventing key cracking and improving data security. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a method for dynamically encrypting and updating network data provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a network data dynamic encryption update system according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] It should be understood that in the embodiments of this application, "at least one" means one or more, and "more than one" means two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the related objects before and after it are in an "or" relationship. "Contains A, B and / or C" means containing any one, two, or three of A, B, and C.

[0019] It should be understood that in the embodiments of this application, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0020] To address the technical problems existing in related technologies, embodiments of this application provide a method, system, device, and storage medium for dynamic encryption and updating of network data.

[0021] The network data dynamic encryption update method provided in this application can be executed by an electronic device, which can be a terminal or a server. The terminal can be a smartphone, tablet, laptop, or other similar device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. It is understood that this application does not specifically limit the executing entity of the network data dynamic encryption update method.

[0022] The technical solution of this application will be described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments described below are used to explain the technical solution of this application and are not intended to limit actual use.

[0023] To address the technical problems existing in related technologies, embodiments of this application provide a method for dynamically encrypting and updating network data, such as... Figure 1 As shown, Figure 1 This is a flowchart of a method for dynamically encrypting and updating network data according to an embodiment of this application. It should be noted that the steps shown may be executed in a different logical order than those shown in the flowchart. The method may include the following steps S101 to S108.

[0024] Step S101: Generate the encryption key by combining a quantum random number generator and a machine learning algorithm.

[0025] In one alternative embodiment, a combination of a quantum random number generator and a machine learning algorithm is used to generate the encryption key using the following formula: ; in, This represents the encryption key. Random numbers generated by a quantum random number generator. For machine learning algorithms based on network parameters The processing results obtained This is a combination function.

[0026] In this step, an encryption key is generated using a combination of quantum random number generators and machine learning algorithms. The quantum random number generator utilizes quantum mechanics principles to produce truly random numbers, providing a high degree of randomness for the key. The machine learning algorithm then performs real-time analysis of various parameters in the network environment, such as network traffic, user behavior patterns, and device status. Based on the analysis results, it optimizes the quantum random numbers to generate high-strength encryption keys that meet current network security requirements. For example, by analyzing the time series of network traffic, potential attack periods can be predicted, and more complex keys can be generated during those periods.

[0027] In another optional embodiment, the network data dynamic encryption update method provided in this application further includes the following steps: Utilize blockchain technology to store encryption keys; distribute encryption keys based on secure multi-party computation technology.

[0028] In one specific embodiment, storing encryption keys using blockchain technology includes: The encryption key is divided into multiple fragments, each fragment is stored on a different blockchain node, and each fragment is encrypted.

[0029] In a more specific embodiment, the Ethereum blockchain is used, and the key is divided into n fragments, each of which is stored in a different Ethereum account address.

[0030] In one specific embodiment, it further includes: generating a unique identifier and access permissions for each fragment.

[0031] The consensus mechanism of blockchain ensures the integrity and consistency of keys, allowing only authorized devices or users to obtain and assemble the corresponding key fragments. For example, access permissions can be controlled through smart contracts.

[0032] In one specific embodiment, during the distribution process, the key is divided into multiple shares and sent to different recipients. This requires the recipients to collaborate in computation to recover the complete key; a key share obtained by any single party cannot be used to decrypt data. For example, an unintentional transmission protocol can be used to send key shares to the recipients, allowing them to recover the key through collaboration with other recipients without knowing their shares.

[0033] Step S102: Real-time monitoring and analysis of the application scenarios of the original network data to obtain scenario analysis results.

[0034] In one alternative embodiment, the scenario analysis results include at least one of the following: the data type of the original network data, transmission rate requirements, and security level requirements. For example, the data type includes at least one of the following: text, images, and video, etc.

[0035] Step S103: Perform real-time evaluation of the encryption algorithm to obtain the algorithm evaluation result.

[0036] In one optional embodiment, the scenario analysis results include at least one of the following: encryption strength, encryption speed, and resource consumption.

[0037] In one specific embodiment, encryption strength is measured by calculating the ability of the encryption algorithm to resist known attack methods, encryption speed is evaluated by the time required to encrypt a certain amount of data in a specific hardware environment, and resource consumption is determined by monitoring the usage of resources such as CPU (Central Processing Unit) and memory during the operation of the encryption algorithm.

[0038] Step S104: Select an encryption algorithm based on the scenario analysis results and algorithm evaluation results to obtain the target encryption algorithm.

[0039] In one alternative embodiment, a decision tree algorithm or a neural network algorithm is used to dynamically select the encryption algorithm most suitable for the current network data. For example, when the data type is financial data and high encryption speed is required, the decision tree algorithm may select the optimized AES (Advanced Encryption Standard) algorithm; when the data transmission environment is complex and the security requirements are extremely high, the neural network algorithm may select a symmetric encryption algorithm.

[0040] In a more specific embodiment, the decision tree makes decisions by constructing a rule tree of "scene features - evaluation indicators - algorithm selection". For example, if the scene analysis result is "data type = financial data, security level requirement = extremely high", and the algorithm evaluation result shows "AES encryption strength = 95 points, RSA (Rivest-Shamir-Adleman) encryption strength = 98 points", then the decision tree outputs "select RSA algorithm". If the scene analysis result is "data type = real-time video, transmission rate requirement = high", and the algorithm evaluation shows "AES encryption speed = 100MB / s, RSA encryption speed = 20MB / s", then the decision tree outputs "select AES algorithm".

[0041] Neural networks learn the correlation between scene features and the optimal algorithm through multi-layer nonlinear mapping.

[0042] For example, the input layer receives quantitative parameters for scenario analysis (such as security level = 0.9, transmission rate requirement = 0.8) and quantitative parameters for algorithm evaluation (such as AES strength = 0.95, efficiency = 0.9); the hidden layer learns feature weights through training (such as security level having higher weight in financial scenarios); the output layer outputs the compatibility probability of each algorithm and selects the encryption algorithm with the highest probability (such as selecting AES if the output is "AES compatibility probability = 0.92").

[0043] Step S105: Encrypt the original network data according to the target encryption algorithm and encryption key to obtain the encrypted network data.

[0044] In another alternative embodiment, before encrypting the original network data according to the target encryption algorithm and encryption key, the method further includes: The raw network data is preprocessed to obtain preprocessed network data. The preprocessing operation includes at least one of the following operations: cleaning, format conversion and compression. Encrypting the original network data according to the target encryption algorithm and encryption key, including: The preprocessed network data is encrypted using the target encryption algorithm and encryption key.

[0045] Data cleaning removes noise and outliers from raw network data; format conversion transforms the raw network data into a format suitable for encryption algorithms; and compressing the raw network data reduces its size, improving encryption efficiency. For example, for text data, regular expressions are used for data cleaning to convert it into a byte array format, and then compression algorithms are applied.

[0046] In one alternative embodiment, if the AES algorithm is selected, the data is encrypted in blocks using a 128-bit or 256-bit encryption key, the plaintext data is divided into blocks of fixed length, and each block is encrypted. In another optional embodiment, after encrypting the original network data according to the target encryption algorithm and encryption key to obtain the encrypted network data, the method further includes: Post-processing operations are performed on encrypted network data to obtain target data. The post-processing operations include at least one of the following: adding encryption identifiers, digital signatures, and integrity verification.

[0047] An encryption identifier indicates that data has been encrypted and the type of encryption algorithm used. A digital signature verifies the source and integrity of the data. Integrity verification ensures that the data has not been tampered with during encryption by calculating the hash value of the data and comparing it with the hash value before encryption. For example, using SHA (Secure Hash Algorithm)-256 to calculate the hash value of the data, and after ensuring that the data has not been tampered with, a digital signature is generated using the target encryption algorithm. Step S106: Monitor security threats in the network in real time and obtain alarm information.

[0048] By using devices or systems such as IDS (Intrusion Detection Systems), firewalls, and security intelligence platforms, security threats in the network can be monitored in real time to obtain alert information.

[0049] In one optional embodiment, the monitored content includes, but is not limited to, network attack behaviors, abnormal network traffic, and data leakage risks. Network attack behaviors include, but are not limited to, DDoS (Distributed Denial of Service) attacks and malware propagation. For example, the IDS identifies potential attack behaviors and sends alerts. Step S107: Determine whether to update the encryption method based on the alarm information.

[0050] In one alternative embodiment, a risk value is determined based on alarm information, data attributes, and system status, and a decision is made on whether to update the encryption method based on the risk value.

[0051] In one specific embodiment, an update is triggered when the risk value exceeds a preset threshold (e.g., 80 points).

[0052] Step S108: If the encryption method is determined to be updated, continue to execute the step of generating an encryption key by combining a quantum random number generator and a machine learning algorithm to encrypt the original network data.

[0053] In this embodiment, an encryption key is generated using a combination of a quantum random number generator and a machine learning algorithm. The application scenario of the original network data is monitored and analyzed in real time to obtain scenario analysis results. The encryption algorithm is evaluated in real time to obtain algorithm evaluation results. An encryption algorithm is selected based on the scenario analysis results and algorithm evaluation results to obtain the target encryption algorithm. The original network data is encrypted using the target encryption algorithm and the encryption key to obtain encrypted network data. Security threats in the network are monitored in real time to obtain alarm information. Based on the alarm information, it is determined whether to update the encryption method. If it is determined to update the encryption method, the step of generating an encryption key using a combination of a quantum random number generator and a machine learning algorithm continues, and the original network data is encrypted. By dynamically generating the encryption key, combined with a quantum random number generator and a machine learning algorithm, the key possesses extremely high randomness and security, effectively preventing key cracking and improving data confidentiality.

[0054] Corresponding to the method for dynamic encryption and updating of network data provided in the embodiments of this application, the embodiments of this application also provide a system for dynamic encryption and updating of network data, such as... Figure 2 As shown, the network data dynamic encryption update system includes: The first generation module 201 is used to generate encryption keys by combining a quantum random number generator and a machine learning algorithm. Analysis module 202 is used to monitor and analyze the application scenarios of raw network data in real time and obtain scenario analysis results; Evaluation module 203 is used to evaluate the encryption algorithm in real time and obtain the algorithm evaluation result; Module 204 is used to select an encryption algorithm based on the scenario analysis results and algorithm evaluation results, thereby obtaining the target encryption algorithm. The encryption module 205 is used to encrypt the original network data according to the target encryption algorithm and the encryption key to obtain the encrypted network data; Monitoring module 206 is used to monitor security threats in the network in real time and obtain alarm information; Module 207 is used to determine whether to update the encryption method based on the alarm information; The second generation module 208 is used to continue executing the step of generating an encryption key by combining a quantum random number generator and a machine learning algorithm, after determining the updated encryption method, to encrypt the original network data.

[0055] In an optional embodiment, it further includes: Utilize blockchain technology to store encryption keys; Encryption keys are distributed based on secure multi-party computation technology.

[0056] In one alternative embodiment, the first generation module is configured to: The encryption key is generated using the following formula, which combines a quantum random number generator and a machine learning algorithm: ; in, This represents the encryption key. Random numbers generated by a quantum random number generator. For machine learning algorithms based on network parameters The processing results obtained, This is a combination function.

[0057] In an optional embodiment, it further includes: The encryption key is divided into multiple fragments, each fragment is stored on a different blockchain node, and each fragment is encrypted.

[0058] In an optional embodiment, it further includes: Generate a unique identifier and access permissions for each fragment.

[0059] In an optional embodiment, it further includes: The raw network data is preprocessed to obtain preprocessed network data. The preprocessing operation includes at least one of the following operations: cleaning, format conversion and compression. Encryption module, used for: The preprocessed network data is encrypted using the target encryption algorithm and encryption key.

[0060] In an optional embodiment, it further includes: Post-processing operations are performed on encrypted network data to obtain target data. The post-processing operations include at least one of the following: adding encryption identifiers, digital signatures, and integrity verification.

[0061] Corresponding to the method for dynamically encrypting and updating network data provided in the embodiments of this application, the embodiments of this application also provide an electronic device for executing the method for dynamically encrypting and updating network data, such as... Figure 3 As shown, the electronic device includes: a processor 301; and a memory 302 for storing a program for a dynamic encryption update method for network data. After the device is powered on and the program for the dynamic encryption update method for network data is run by the processor, the following steps are performed: The encryption key is generated by combining a quantum random number generator with a machine learning algorithm. Real-time monitoring and analysis of the application scenarios of raw network data are performed to obtain scenario analysis results; The encryption algorithm is evaluated in real time to obtain the algorithm evaluation result; Based on the scenario analysis results and algorithm evaluation results, an encryption algorithm is selected to obtain the target encryption algorithm; The original network data is encrypted using the target encryption algorithm and encryption key to obtain the encrypted network data. Real-time monitoring of network security threats and receipt of alerts; Determine whether to update the encryption method based on the alarm information; If the encryption method is determined to be updated, the step of generating an encryption key using a combination of a quantum random number generator and a machine learning algorithm continues to encrypt the original network data.

[0062] In an optional embodiment, after generating the encryption key using a combination of a quantum random number generator and a machine learning algorithm, the method further includes: Utilize blockchain technology to store encryption keys; Encryption keys are distributed based on secure multi-party computation technology.

[0063] In one optional embodiment, the encryption key is generated by combining a quantum random number generator and a machine learning algorithm, including: The encryption key is generated using the following formula, which combines a quantum random number generator and a machine learning algorithm: ; in, This represents the encryption key. Random numbers generated by a quantum random number generator. For machine learning algorithms based on network parameters The processing results obtained, This is a combination function.

[0064] In one alternative embodiment, the encryption key is stored using blockchain technology, including: The encryption key is divided into multiple fragments, each fragment is stored on a different blockchain node, and each fragment is encrypted.

[0065] In an alternative embodiment, after dividing the encryption key into multiple fragments, the method further includes: Generate a unique identifier and access permissions for each fragment.

[0066] In an optional embodiment, before encrypting the original network data according to the target encryption algorithm and encryption key, the method further includes: The raw network data is preprocessed to obtain preprocessed network data. The preprocessing operation includes at least one of the following operations: cleaning, format conversion and compression. Encrypting the original network data according to the target encryption algorithm and encryption key, including: The preprocessed network data is encrypted using the target encryption algorithm and encryption key.

[0067] In one optional embodiment, after encrypting the original network data according to the target encryption algorithm and encryption key to obtain the encrypted network data, the method further includes: Post-processing operations are performed on encrypted network data to obtain target data. The post-processing operations include at least one of the following: adding encryption identifiers, digital signatures, and integrity verification.

[0068] Corresponding to the network data dynamic encryption update method provided in the embodiments of this application, the embodiments of this application also provide a computer-readable storage medium storing a program for the network data dynamic encryption update method, which is executed by a processor to perform the following steps: The encryption key is generated by combining a quantum random number generator with a machine learning algorithm. Real-time monitoring and analysis of the application scenarios of raw network data are performed to obtain scenario analysis results; The encryption algorithm is evaluated in real time to obtain the algorithm evaluation result; Based on the scenario analysis results and algorithm evaluation results, an encryption algorithm is selected to obtain the target encryption algorithm; The original network data is encrypted using the target encryption algorithm and encryption key to obtain the encrypted network data. Real-time monitoring of network security threats and receipt of alerts; Determine whether to update the encryption method based on the alarm information; If the encryption method is determined to be updated, the step of generating an encryption key using a combination of a quantum random number generator and a machine learning algorithm continues to encrypt the original network data.

[0069] In an optional embodiment, after generating the encryption key using a combination of a quantum random number generator and a machine learning algorithm, the method further includes: Utilize blockchain technology to store encryption keys; Encryption keys are distributed based on secure multi-party computation technology.

[0070] In one optional embodiment, the encryption key is generated by combining a quantum random number generator and a machine learning algorithm, including: The encryption key is generated using the following formula, which combines a quantum random number generator and a machine learning algorithm: ; in, This represents the encryption key. Random numbers generated by a quantum random number generator. For machine learning algorithms based on network parameters The processing results obtained, This is a combination function.

[0071] In one alternative embodiment, the encryption key is stored using blockchain technology, including: The encryption key is divided into multiple fragments, each fragment is stored on a different blockchain node, and each fragment is encrypted.

[0072] In an alternative embodiment, after dividing the encryption key into multiple fragments, the method further includes: Generate a unique identifier and access permissions for each fragment.

[0073] In an optional embodiment, before encrypting the original network data according to the target encryption algorithm and encryption key, the method further includes: The raw network data is preprocessed to obtain preprocessed network data. The preprocessing operation includes at least one of the following operations: cleaning, format conversion and compression. Encrypting the original network data according to the target encryption algorithm and encryption key, including: The preprocessed network data is encrypted using the target encryption algorithm and encryption key.

[0074] In one optional embodiment, after encrypting the original network data according to the target encryption algorithm and encryption key to obtain the encrypted network data, the method further includes: Post-processing operations are performed on encrypted network data to obtain target data. The post-processing operations include at least one of the following: adding encryption identifiers, digital signatures, and integrity verification.

[0075] Corresponding to the network data dynamic encryption update method provided in the embodiments of this application, the embodiments of this application also provide a computer program containing instructions, which, when executed by a computer, cause the computer to perform the following steps: The encryption key is generated by combining a quantum random number generator with a machine learning algorithm. Real-time monitoring and analysis of the application scenarios of raw network data are performed to obtain scenario analysis results; The encryption algorithm is evaluated in real time to obtain the algorithm evaluation result; Based on the scenario analysis results and algorithm evaluation results, an encryption algorithm is selected to obtain the target encryption algorithm; The original network data is encrypted using the target encryption algorithm and encryption key to obtain the encrypted network data. Real-time monitoring of network security threats and receipt of alerts; Determine whether to update the encryption method based on the alarm information; If the encryption method is determined to be updated, the step of generating an encryption key using a combination of a quantum random number generator and a machine learning algorithm continues to encrypt the original network data.

[0076] In an optional embodiment, after generating the encryption key using a combination of a quantum random number generator and a machine learning algorithm, the method further includes: Utilize blockchain technology to store encryption keys; Encryption keys are distributed based on secure multi-party computation technology.

[0077] In one optional embodiment, the encryption key is generated by combining a quantum random number generator and a machine learning algorithm, including: The encryption key is generated using the following formula, which combines a quantum random number generator and a machine learning algorithm: ; in, This represents the encryption key. Random numbers generated by a quantum random number generator. For machine learning algorithms based on network parameters The processing results obtained This is a combination function.

[0078] In one alternative embodiment, the encryption key is stored using blockchain technology, including: The encryption key is divided into multiple fragments, each fragment is stored on a different blockchain node, and each fragment is encrypted.

[0079] In an alternative embodiment, after dividing the encryption key into multiple fragments, the method further includes: Generate a unique identifier and access permissions for each fragment.

[0080] In an optional embodiment, before encrypting the original network data according to the target encryption algorithm and encryption key, the method further includes: The raw network data is preprocessed to obtain preprocessed network data. The preprocessing operation includes at least one of the following operations: cleaning, format conversion and compression. Encrypting the original network data according to the target encryption algorithm and encryption key, including: The preprocessed network data is encrypted using the target encryption algorithm and encryption key.

[0081] In one optional embodiment, after encrypting the original network data according to the target encryption algorithm and encryption key to obtain the encrypted network data, the method further includes: Post-processing operations are performed on encrypted network data to obtain target data. The post-processing operations include at least one of the following: adding encryption identifiers, digital signatures, and integrity verification.

[0082] It should be noted that for a detailed description of the network data dynamic encryption update system, electronic device, computer-readable storage medium and computer program provided in the embodiments of this application, please refer to the relevant description of the embodiments of the dehazing image generation method provided in the embodiments of this application, which will not be repeated here.

[0083] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0084] In a typical configuration, an electronic device includes one or more processors (Central Processing Units), input / output interfaces, network interfaces, and memory.

[0085] Memory may include non-persistent storage in computer-readable media, such as random access memory and / or non-volatile memory, like read-only memory or flash memory. Memory is an example of computer-readable media.

[0086] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable operations, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PCM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital video disc (DMCD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media, such as modulated data signals and carrier waves.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, compact disc read-only memory, optical storage, etc.) containing computer-usable program code.

[0088] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in this application.

Claims

1. A method for dynamically encrypting and updating network data, characterized in that, include: The encryption key is generated by combining a quantum random number generator with a machine learning algorithm. Real-time monitoring and analysis of the application scenarios of raw network data are performed to obtain scenario analysis results; The encryption algorithm is evaluated in real time to obtain the algorithm evaluation result; Based on the scenario analysis results and the algorithm evaluation results, an encryption algorithm is selected to obtain the target encryption algorithm; The original network data is encrypted using the target encryption algorithm and the encryption key to obtain encrypted network data. Real-time monitoring of network security threats and receipt of alerts; Determine whether to update the encryption method based on the alarm information; If the encryption method is determined to be updated, the step of generating an encryption key using a combination of a quantum random number generator and a machine learning algorithm continues to be performed to encrypt the original network data.

2. The method for dynamically encrypting and updating network data according to claim 1, characterized in that, After generating the encryption key using a combination of quantum random number generator and machine learning algorithm, the method further includes: The encryption key is stored using blockchain technology; The encryption key is distributed based on secure multi-party computation technology.

3. The method for dynamically encrypting and updating network data according to claim 1, characterized in that, The method of generating encryption keys by combining quantum random number generators and machine learning algorithms includes: The encryption key is generated using the following formula, which combines a quantum random number generator and a machine learning algorithm: ； in, This refers to the encryption key. Random numbers generated by a quantum random number generator. For machine learning algorithms based on network parameters The processing results obtained, This is a combination function.

4. The method for dynamically encrypting and updating network data according to claim 2, characterized in that, The use of blockchain technology to store the encryption key includes: The encryption key is divided into multiple fragments, each fragment is stored on a different blockchain node, and each fragment is encrypted.

5. The method for dynamically encrypting and updating network data according to claim 3, characterized in that, After dividing the encryption key into multiple fragments, the process further includes: Generate a unique identifier and access permissions for each fragment.

6. The method for dynamically encrypting and updating network data according to claim 1, characterized in that, Before encrypting the original network data according to the target encryption algorithm and the encryption key, the method further includes: The original network data is preprocessed to obtain preprocessed network data, wherein the preprocessing operation includes at least one of the following operations: cleaning, format conversion and compression. The step of encrypting the original network data according to the target encryption algorithm and the encryption key includes: The preprocessed network data is encrypted according to the target encryption algorithm and the encryption key.

7. The method for dynamically encrypting and updating network data according to claim 1, characterized in that, After encrypting the original network data according to the target encryption algorithm and the encryption key to obtain the encrypted network data, the method further includes: The encrypted network data is post-processed to obtain the target data. The post-processing operation includes at least one of the following operations: adding an encryption identifier, digital signature, and integrity verification.

8. A network data dynamic encryption update system, characterized in that, include: The first generation module is used to generate encryption keys by combining a quantum random number generator and a machine learning algorithm. The analysis module is used to monitor and analyze the application scenarios of raw network data in real time and obtain scenario analysis results; The evaluation module is used to evaluate the encryption algorithm in real time and obtain the algorithm evaluation results; The selection module is used to select an encryption algorithm based on the scenario analysis results and the algorithm evaluation results to obtain the target encryption algorithm. An encryption module is used to encrypt the original network data according to the target encryption algorithm and the encryption key to obtain encrypted network data; The monitoring module is used to monitor security threats in the network in real time and obtain alarm information; The determination module is used to determine whether to update the encryption method based on the alarm information; The second generation module is used to continue executing the step of generating an encryption key by combining a quantum random number generator and a machine learning algorithm, when the encryption method is determined to be updated, so as to encrypt the original network data.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the network data dynamic encryption update method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the network data dynamic encryption and update method according to any one of claims 1-7.

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