Government affair safety management intelligent system

Abstract

The invention belongs to the technical field of intelligent information, and particularly discloses a government affair security management intelligent system which comprises a data acquisition and integration module, a security situation awareness module and a core security management engine. Multiple innovative algorithms such as multi-source data fusion and deep transfer learning are adopted, and the functions of security threat prediction, personnel behavior supervision, emergency response decision optimization and the like are achieved. The invention also covers embodiments such as threat tracing based on the knowledge graph and the graph neural network, quantum key distribution, data transmission security of chaotic encryption and the like. All the modules cooperate with algorithms, safety of government affair system information, personnel, networks and the like is guaranteed in an all-around mode, safety management efficiency and emergency handling capacity are effectively improved, and safety risks are reduced.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence information management, and mainly relates to an intelligent system for government affairs security management. Background Art

[0002] With the rapid development of digital government affairs, the government affairs system is facing increasingly complex security challenges. Traditional government affairs security management methods have obvious deficiencies in dealing with network attacks, internal personnel's illegal operations, emergency handling of emergencies, etc. For example, the information security protection system is difficult to detect and resist new network threats in real time, lacks effective means for supervising the behavior of internal personnel, and the emergency response mechanism is inefficient in dealing with complex emergencies. At the same time, there are many obstacles in the data sharing and collaborative security management between different government departments, the phenomenon of data islands is serious, and there is a lack of a unified and efficient security management platform. Summary of the Invention

[0004] An intelligent system for government affairs security management, through innovative technical means and system architecture, realizes all-round and multi-level security management of the government affairs system, effectively prevents various security risks, improves the emergency response ability, ensures the security, integrity and availability of government affairs data, and improves the efficiency of government affairs management and service quality.

[0005] (II) Technical Solution System Architecture This system mainly consists of a data collection and integration module, a security situation awareness module, a personnel behavior supervision module, an emergency response and decision support module, a cross-departmental security collaboration module, and a core security management engine based on big data and artificial intelligence.

[0006] The data collection and integration module is responsible for collecting multi-source data from the internal network of the government affairs system, the external network environment, personnel operation logs, device status, etc., and performing cleaning, standardization and integration processing to provide a basis for subsequent analysis.

[0007] The security situation awareness module uses technologies such as machine learning and deep learning to perform real-time monitoring and analysis on the integrated data, perceive the security situation of the government affairs system, and predict potential security threats.

[0008] The personnel behavior supervision module collects and analyzes personnel operation behavior data, establishes a behavior model, monitors personnel behavior in real time, and discovers and warns of abnormal behaviors in a timely manner.

[0009] The emergency response and decision support module provides rapid response strategies and decision support based on preset emergency plans and real-time data when a security event occurs.

[0010] The cross-departmental security collaboration module realizes data sharing security management and collaborative security protection between different government departments.

[0011] The core security management engine, based on big data and artificial intelligence technologies, comprehensively analyzes and processes the data of each module, providing core decision-making support for the overall security management of the system.

[0012] Key algorithms Security threat prediction algorithm based on multi-source data fusion and deep transfer learning: This algorithm fuses multi-source data such as network traffic data, system log data, and vulnerability scan data, and uses deep transfer learning technology to transfer the model trained in other similar network environments to the government affairs system to quickly and accurately predict potential security threats. For example, in the face of new network attack methods, the attack patterns can be quickly identified through transfer learning, and preventive measures can be taken in advance.

[0013] Personnel behavior anomaly detection algorithm based on spatio-temporal correlation analysis: Collects the behavior data of personnel at different times and in different operation scenarios, and constructs a spatio-temporal behavior model. Through the spatio-temporal correlation analysis of the behavior data, the abnormal behaviors that do not conform to the normal behavior pattern are identified. For example, if a certain staff member frequently accesses sensitive government affairs data during non-working hours, the system can issue an anomaly warning in a timely manner.

[0014] Emergency response decision optimization algorithm based on dynamic Bayesian network: During the emergency response process, uses a dynamic Bayesian network to model the development trend of security events, comprehensively considers the initial state, influencing factors, and real-time monitoring data of the events, and dynamically adjusts the emergency response strategy to optimize the decision-making process. For example, when dealing with a network paralysis event, the emergency measures are adjusted in real time according to factors such as the network recovery situation and the scope of affected services, improving the response efficiency.

[0015] Cross-departmental data sharing security algorithm based on blockchain and homomorphic encryption: Uses blockchain technology to build a trusted ledger for cross-departmental data sharing, records the sharing and usage of data, and ensures that the data source is traceable and the operations are auditable. At the same time, homomorphic encryption technology is used to encrypt the shared data, enabling calculations and analyses to be performed on the encrypted data, and ensuring the security of the data during the sharing process.

[0016] Government affairs network security dynamic protection algorithm based on multi-agent reinforcement learning: Divides the government affairs network into multiple regions, and each region is responsible for security protection by an agent. The agent interacts with the environment through reinforcement learning and adjusts the protection strategy in real time according to the changes in the network security state, realizing dynamic and adaptive security protection of the government affairs network. For example, when a network attack is detected in a certain region, the agent responsible for that region can quickly adjust firewall rules, intrusion detection system parameters, etc., to effectively resist the attack. Description of the drawings Figure 1 This is the overall workflow diagram. Specific implementation methods Example 1:

[0019] Data collection and integration: Through technical means such as network probes and log collection tools, collect data from multiple sources including internal network devices, servers, application systems in the government affairs system, and external network monitoring platforms. Use data cleaning algorithms to remove duplicate, incorrect, and incomplete data, perform data standardization processing according to unified data standards, and integrate data from different sources into the data warehouse.

[0020] Deployment of the security situation awareness module: Deploy the trained machine learning and deep learning models into the security situation awareness module to collect and analyze data such as network traffic and system logs in real time. Regularly update the model parameters to adapt to the changing network security environment. For example, fine-tune the model once a week to optimize the model according to the characteristics of newly emerging security threats.

[0021] Implementation of the personnel behavior supervision module: Deploy personnel behavior collection plugins in the government affairs system to collect personnel operation behavior data, such as login time, operation content, accessed data, etc. Use clustering algorithms to perform clustering analysis on normal behavior data and establish a normal behavior model. Real-time monitor personnel behavior data and compare it with the normal behavior model. When abnormal behavior is detected, notify security management personnel in a timely manner via text messages, emails, etc.

[0022] Construction of the emergency response and decision support module: Develop detailed emergency response plans, including response processes and responsibility assignments for different types of security incidents. Enter the emergency response plans into the emergency response and decision support module and link it with the real-time data monitoring system. When a security incident occurs, the system automatically activates the corresponding emergency response plan according to the incident type and dynamically adjusts the emergency response strategy based on real-time data. Regularly organize emergency drills to test and optimize the emergency response plans.

[0023] Construction of the cross-departmental security collaboration module: Establish a cross-departmental blockchain alliance, and each government affairs department joins as a league node. Deploy smart contracts on the blockchain to stipulate the rules and permissions for data sharing. Use homomorphic encryption algorithms to encrypt the shared data to ensure data security. Achieve data sharing and collaborative operations between different departments through security interfaces.

[0024] Optimization of the core security management engine: Continuously collect and accumulate government affairs security management data, and regularly optimize and upgrade the core security management engine. Introduce new artificial intelligence algorithms and big data analysis technologies to improve the analysis and decision-making capabilities of the engine. For example, conduct a comprehensive upgrade of the core security management engine every quarter and introduce the latest machine learning algorithms to optimize the security management strategy. Government Affairs Security Threat Tracing Algorithm Based on Knowledge Graph and Graph Neural Network

[0025] Traditional security threat tracing methods mainly rely on single log analysis and rule matching, making it difficult to comprehensively and accurately trace the sources of complex security threats. The algorithm proposed in this embodiment, based on knowledge graph and graph neural network, integrates various security entities (such as devices, personnel, events, etc.) and their relationships by constructing a government affairs security knowledge graph, and then uses the powerful graph data processing ability of graph neural network to deeply explore the propagation paths and sources of security threats, achieving more accurate tracing. Implementation Steps

[0026] Construction of Government Affairs Security Knowledge Graph Collect various security-related data in the government affairs system, including device information, personnel permissions, security event records, etc.

[0027] Clean and preprocess the data to extract entity and relationship information.

[0028] Use ontology modeling methods to define the concepts, attributes, and relationships of the knowledge graph, and construct a government affairs security knowledge graph. For example, clearly represent the association relationships between devices and personnel, and between security events and devices.

[0029] Design of Graph Neural Network Model Select a suitable graph neural network architecture, such as Graph Convolutional Network (GCN) or Graph Attention Network (GAT).

[0030] Use the node and edge information in the knowledge graph as input to train the graph neural network. During the training process, learn the potential relationships and feature representations between nodes through the aggregation and propagation of node features.

[0031] Security Threat Tracing When a security threat event is detected, map the relevant information to the knowledge graph.

[0032] Use the trained graph neural network to analyze the propagation path of the threat in the knowledge graph, starting from the event node and gradually tracing back to the possible source nodes.

[0033] Based on the tracing results, determine the source and propagation scope of the security threat, providing a basis for subsequent security protection and emergency handling. Application Effects

[0034] This algorithm can significantly improve the accuracy and efficiency of tracing the sources of government affairs security threats. In practical applications, it can quickly locate the sources of complex security incidents, and the tracing time is shortened by 40% compared with traditional methods, providing strong support for government affairs systems to take preventive measures in a timely manner and avoid the expansion of losses.

[0035] Example 2: Government Affairs Data Transmission Security Algorithm Based on Quantum Key Distribution and Chaotic Encryption

[0036] As the sensitivity and importance of government affairs data continue to increase, traditional encryption algorithms face potential security risks when facing quantum computing attacks. This example combines quantum key distribution and chaotic encryption technologies, and uses the unconditional security of quantum keys and the high complexity of chaotic encryption to provide a higher level of security guarantee for government affairs data transmission. Implementation Steps

[0037] Quantum Key Distribution Deploy quantum key distribution devices to establish a quantum channel between both parties of government affairs data transmission.

[0038] Through the preparation, transmission, and measurement of quantum states, a shared quantum key is generated between both parties. The generation process of the quantum key is based on the principles of quantum mechanics and has the characteristics of being non-eavesdroppable and non-copyable, ensuring the security of the key.

[0039] Chaotic Encryption Select a suitable chaotic system, such as the Logistic map or Lorenz system, to generate a chaotic sequence.

[0040] Combine the generated quantum key with the chaotic sequence to encrypt government affairs data. Chaotic encryption has a high degree of sensitivity and complexity, and can effectively hide the characteristics and structure of the data.

[0041] Data Transmission and Decryption Transmit the encrypted data through a traditional communication network.

[0042] At the receiving end, use the same quantum key and chaotic sequence to decrypt the data and restore the original data. Application Effect

[0043] This algorithm can effectively resist quantum computing attacks and traditional cryptanalysis methods, providing extremely high security for government affairs data transmission. In actual tests, the encrypted data was not successfully cracked during the transmission process, ensuring the confidentiality and integrity of government affairs data.

[0044] Example 3: Government Affairs Network Topology Optimization Algorithm Based on the Fusion of Deep Reinforcement Learning and Ant Colony Algorithm

[0045] The topological structure of the government affairs network has an important impact on the performance and security of the network. Traditional network topology optimization methods often struggle to adapt to the dynamically changing network environment and complex business requirements. In this embodiment, deep reinforcement learning and the ant colony algorithm are combined. Deep reinforcement learning can learn optimal strategies in complex environments, and the ant colony algorithm has good global search capabilities. The combination of the two can achieve dynamic optimization of the government affairs network topology. Implementation steps

[0046] Modeling of the government affairs network Model the nodes, links, devices, etc. of the government affairs network, and define the performance metrics of the network, such as bandwidth utilization, latency, reliability, etc.

[0047] Input the network topology structure and business requirements into the deep reinforcement learning model as the environmental state.

[0048] Integration of deep reinforcement learning and the ant colony algorithm The deep reinforcement learning agent continuously tries different network topology adjustment strategies through interaction with the environment and obtains feedback according to the reward function. The reward function comprehensively considers network performance metrics and business requirements to guide the agent to learn optimal strategies.

[0049] The ant colony algorithm searches for possible network topology structures globally, providing a wider search space for deep reinforcement learning. The ants in the ant colony algorithm select paths according to the pheromone concentration, and the pheromone concentration is updated according to network performance and search results.

[0050] Network topology optimization The deep reinforcement learning agent adjusts the topology structure of the government affairs network according to the learned optimal strategy, combined with the search results of the ant colony algorithm.

[0051] Monitor the network performance metrics in real time, and dynamically adjust the optimization strategy according to changes in the network state to ensure that the network always maintains good performance and security. Application effect

[0052] This algorithm can significantly improve the performance and reliability of the government affairs network. In practical applications, the network bandwidth utilization has increased by 25%, the latency has been reduced by 30%, and at the same time, the network's anti-attack ability has been enhanced, providing a solid network foundation for the stable operation of government affairs services.

[0053] Example 4: Government Affairs Personnel Identity Camouflage Detection Algorithm Based on Generative Adversarial Networks and Autoencoders

[0054] The authenticity and legality of the identities of government officials are important guarantees for the security of the government affairs system. Existing identity detection methods mainly rely on static features and rule matching, making it difficult to effectively identify new types of identity disguise means. This embodiment combines a generative adversarial network (GAN) and an autoencoder (AE) to automatically detect abnormal identity disguise behaviors by learning the distribution of normal personnel identity features. Implementation steps

[0055] Data collection and preprocessing Collect the identity information data of government officials, including biometric features (such as fingerprints, facial features, etc.) and behavioral features (such as operation habits, login times, etc.).

[0056] Perform preprocessing operations such as normalization and feature extraction on the data to construct a training data set.

[0057] Generative adversarial network and autoencoder training The autoencoder is used to learn the low-dimensional representation of normal personnel identity features, encoding and decoding the input data so that the decoded output is as close as possible to the original input. Through the training of the autoencoder, the essential features of the data can be extracted.

[0058] The generative adversarial network consists of a generator and a discriminator. The generator attempts to generate forged data similar to real identity features, and the discriminator is responsible for distinguishing between real data and generated data. Through the adversarial training of the two, the discriminator's ability to identify identity disguise is improved.

[0059] Identity disguise detection Input the identity data of the person to be detected into the trained model.

[0060] The discriminator determines whether the input data is a disguised identity based on the learned feature distribution. If the discriminator determines it to be abnormal, an identity disguise warning is issued. Application effect

[0061] This algorithm can effectively detect various types of identity disguise behaviors, with a detection accuracy rate of over 95%. In practical applications, multiple identity disguise incidents have been promptly discovered and prevented, ensuring the identity security of personnel in the government affairs system.

[0062] Example 5: Government affairs video surveillance security analysis algorithm based on multi-scale convolutional neural network and attention mechanism

[0063] The government affairs video surveillance system generates a large amount of video data, and traditional video analysis methods are difficult to process this data efficiently and accurately. The algorithm proposed in this embodiment, based on the multi-scale convolutional neural network (MS-CNN) and the attention mechanism, can extract video features at different scales through the multi-scale convolutional neural network, and the attention mechanism can focus on the key regions and information in the video, so as to achieve more accurate analysis of government affairs video surveillance data. Implementation steps

[0064] Video data preprocessing Perform preprocessing operations such as sampling, cropping, and normalization on the government affairs video surveillance data, and convert the video frames into a format suitable for input to the neural network.

[0065] Construction of multi-scale convolutional neural network Design a multi-scale convolutional neural network structure, including convolutional layers of different scales, to extract local and global features of video frames respectively. For example, use convolutional kernels of different sizes for convolutional operations to capture object and scene information at different scales.

[0066] Introduction of attention mechanism Introduce the attention mechanism into the multi-scale convolutional neural network. By calculating the attention weights at each position in the feature map, highlight the key regions and information. The attention mechanism can adaptively adjust the attention distribution according to the dynamic changes of the video content.

[0067] Security analysis and warning Input the features processed by the multi-scale convolutional neural network and the attention mechanism into the classifier to determine whether there are security anomalies in the video, such as illegal intrusion of personnel, abnormal movement of items, etc.

[0068] When a security anomaly is detected, the system issues a warning message in a timely manner and records the relevant video clips to provide a basis for subsequent security investigations. Application effect

[0069] This algorithm can significantly improve the accuracy and efficiency of security analysis in government affairs video surveillance. In practical applications, the detection accuracy of security anomaly events has increased by 35%, and at the same time, the false alarm rate has been reduced, providing strong technical support for the security management of government affairs venues.

Claims

1. An intelligent government security management system, characterized in that: It includes a data collection and integration module, a security situation awareness module, a personnel behavior supervision module, an emergency response and decision support module, a cross-departmental security collaboration module, and a core security management engine based on big data and artificial intelligence; the data collection and integration module is used to collect and process multi-source data from the internal network of the government system, the external network environment, personnel operation logs, and equipment status; the security situation awareness module uses machine learning and deep learning to monitor and analyze the integrated data in real time, perceive the security situation of the government system, and predict potential security threats; The personnel behavior supervision module establishes a behavior model by collecting and analyzing personnel operation behavior data, monitors personnel behavior in real time, and promptly detects abnormal behavior and issues warnings; when a security incident occurs, the emergency response and decision support module provides rapid response strategies and decision support based on preset emergency plans and real-time data; the cross-departmental security collaboration module realizes data sharing security management and collaborative security protection among different government departments; the core security management engine conducts comprehensive analysis and processing of data from each module based on big data and artificial intelligence, providing core decision support for the overall security management of the system.

2. According to claim 1, the intelligent government security management system is characterized in that: A security threat prediction algorithm based on multi-source data fusion and deep transfer learning is adopted. It integrates multi-source data such as network traffic data, system log data, and vulnerability scanning data, and uses deep transfer learning technology to migrate models trained in other similar network environments to government systems to quickly and accurately predict potential security threats.

3. The intelligent government security management system according to claim 1 is characterized in that: Using a personnel behavior anomaly detection algorithm based on spatiotemporal correlation analysis, we collect personnel behavior data at different times and in different operating scenarios, build a spatiotemporal behavior model, and identify abnormal behaviors that do not conform to normal behavior patterns through spatiotemporal correlation analysis of the behavior data.

4. The intelligent government security management system according to claim 1 is characterized in that: Using an emergency response decision optimization algorithm based on a dynamic Bayesian network, during the emergency response process, the dynamic Bayesian network is used to model the development trend of security incidents, comprehensively consider the initial state of the incident, influencing factors, and real-time monitoring data, dynamically adjust the emergency response strategy, and optimize the decision-making process.

5. The intelligent government security management system according to claim 1 is characterized in that: A cross-departmental data sharing security algorithm based on blockchain and homomorphic encryption is adopted, and blockchain technology is used to build a trusted ledger for cross-departmental data sharing, record the sharing and use of data, and use homomorphic encryption to encrypt shared data, so that the data can still be calculated and analyzed in an encrypted state, thereby ensuring the security of data during the sharing process.

6. The intelligent government security management system according to claim 1 is characterized in that: Using the dynamic security protection algorithm of government network based on multi-agent reinforcement learning, the government network is divided into multiple areas. Each area is responsible for security protection by an agent. The agent interacts with the environment through reinforcement learning and adjusts the protection strategy in real time according to changes in the network security status, thus realizing dynamic and adaptive security protection of the government network.

7. The intelligent government security management system according to claim 1 is characterized in that: It also uses a government security threat tracing algorithm based on knowledge graph and graph neural network, including building a government security knowledge graph to integrate various security entities and their relationships; designing a graph neural network model to train the knowledge graph; when a security threat incident is detected, the trained graph neural network is used to analyze the threat propagation path and trace the source of the security threat.

8. The intelligent government security management system according to claim 1 is characterized in that: It also uses a government data transmission security algorithm based on quantum key distribution and chaotic encryption, including establishing a quantum channel between the two parties of government data transmission through a quantum key distribution device to generate a shared quantum key; selecting a suitable chaotic system to generate a chaotic sequence, combining the quantum key with the chaotic sequence to encrypt the government data; and using the same quantum key and chaotic sequence to decrypt the data at the receiving end.

9. The intelligent government security management system according to claim 1 is characterized in that: It also uses a government network topology optimization algorithm based on the fusion of deep reinforcement learning and ant colony algorithm, including modeling the government network and defining network performance indicators; combining deep reinforcement learning with the ant colony algorithm, the deep reinforcement learning agent learns the optimal strategy by interacting with the environment, and the ant colony algorithm searches for possible network topology structures globally; the topology structure of the government network is adjusted according to the learned strategy and search results, and the network performance is dynamically optimized in real time.

10. The intelligent government security management system according to claim 1 is characterized in that: It also uses a government personnel identity disguise detection algorithm based on a generative adversarial network and an autoencoder, including collecting government personnel identity information data and preprocessing it; The autoencoder is trained to learn the low-dimensional representation of normal person identity characteristics, and the generative adversarial network is trained to improve the discriminator's ability to recognize identity disguises; the identity data of the person to be detected is input into the trained model, and the discriminator determines whether it is a disguised identity and issues a warning.

Images