Intelligent attack system based on AI

Abstract

The invention discloses an AI-based intelligent attack system, which belongs to the field of network security, and comprises a data collection module for collecting a network topology structure, equipment configuration information, user behavior data and security policy setting of a target network by utilizing a web crawler and traffic sniffing technology, and providing basic data for subsequent analysis. And the intelligent analysis module is used for carrying out deep analysis on the collected data by adopting a graph neural network algorithm in deep learning, and constructing a digital twinborn model of the target network. The network crawler and traffic sniffing technology is comprehensively applied, multi-dimensional information of the target network is collected, and a basis is provided for deep analysis. A digital twinborn model is constructed through a deep learning algorithm, security vulnerabilities are mined, and attack pertinence is improved. On the basis of the intelligent analysis result, the reinforcement learning algorithm is applied, according to the actual situation of the target network, attack tools, paths and sequences are intelligently selected, diversified strategies are generated, and attack effectiveness is ensured.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to an AI-based intelligent attack system. Background Art

[0002] With the rapid development of network technology, network security has become increasingly important, and network attack and defense technologies have been continuously evolving. On the one hand, the network systems of organizations such as enterprises and government agencies have become increasingly complex, including a large number of network devices, servers, and rich user data, and the network topology and security policies have also become more diverse. Facing the increasingly perfect defense system, the effect of traditional network attack means has gradually decreased, and it is difficult to break through modern security protection mechanisms. On the other hand, significant progress has been made in artificial intelligence technology in various fields, providing new ideas and methods for the innovation of network attack technology. Technologies such as deep learning and reinforcement learning can process massive data, mine potential patterns and relationships in the data, and provide powerful technical support for data collection, analysis, strategy formulation, and execution in network attacks.

[0003] Against this background, the AI-based intelligent attack system came into being, aiming to use advanced AI technology to break through the existing network defense system and meet the attacker's needs for efficient, concealed, and precise attacks. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an AI-based intelligent attack system, which solves the problems raised in the above background art.

[0005] Technical Solution: To solve the above technical problems, according to one aspect of the present invention, more specifically, an AI-based intelligent attack system includes: a data collection module: using web crawler and traffic sniffing technologies to collect the network topology, device configuration information, user behavior data, and security policy settings of the target network, providing basic data for subsequent analysis.

[0006] An intelligent analysis module: adopting the graph neural network algorithm in deep learning to deeply analyze the collected data, construct a digital twin model of the target network, simulate the normal operation state and potential changes, and mine security vulnerabilities and weak links by comparing historical attack data.

[0007] An attack strategy generation module: based on the intelligent analysis results, using the reinforcement learning algorithm to generate the optimal attack strategy, including selecting attack tools, determining attack paths, and arranging attack sequences, and generating diversified strategies to cope with defense mechanisms.

[0008] An attack execution module: according to the generated attack strategy, calling corresponding attack tools and technologies, such as vulnerability scanning tools, malware propagation, DDoS attacks, etc., to launch attacks on the target network, and real-time monitoring the attack effect and dynamically adjusting the strategy.

[0009] Attack effect evaluation module: After the attack is executed, it evaluates the attack effect by analyzing indicators such as the response of the target network, data leakage situation, and service interruption degree, and feeds back the results to the intelligent analysis module and the attack strategy generation module to optimize the attack system.

[0010] Furthermore, the system has the ability of adaptive attack, which can automatically learn and dynamically change the attack strategy and method according to the real-time changes of the target network and the adjustment of defense measures, so as to avoid being recognized and blocked by traditional defense systems.

[0011] Furthermore, the web crawler will simulate the browsing behavior of normal users, deeply traverse the target network, capture the public network topology structure and device configuration details, and not miss any possible network paths. The traffic sniffing is deployed at key network nodes to analyze the real-time flowing network traffic, obtain user behavior data, including user login time, access frequency, common operations, etc., and security policy settings, such as firewall rules, intrusion detection system configurations, etc., to lay a solid data foundation for subsequent in-depth analysis.

[0012] Furthermore, the attack strategy generation module will intelligently select attack tools according to the actual situation of the target network, such as dedicated scanning tools for specific vulnerabilities, carefully customized malware, etc. When determining the attack path, fully consider the network topology structure, avoid high-risk areas, and select the most concealed and efficient path. At the same time, arrange a reasonable attack order, first conduct a tentative attack, and then gradually increase the attack intensity according to the reaction of the target network. Generate diversified strategies for different defense mechanisms to ensure the effectiveness of the attack.

[0013] Furthermore, during the attack process, the attack execution module monitors the attack effect in real time, and dynamically adjusts the attack strategy by analyzing the response data and traffic changes of the target network. Once it is found that the attack is blocked, immediately switch the attack method to ensure the continuous progress of the attack.

[0014] Furthermore, the attack effect evaluation module judges the activation situation of its defense mechanism by analyzing the response of the target network; counts the data leakage situation and evaluates the results of the attack in terms of data acquisition; measures the degree of service interruption to determine the impact on the target network business. Feed back these evaluation results to the intelligent analysis module and the attack strategy generation module in a timely manner, so as to optimize the attack system and improve the success rate of subsequent attacks.

[0015] Furthermore, the system uses AI technology to intelligently disguise the attack behavior, making it appear as normal network traffic or user behavior, reducing the detection risk, and increasing the concealment and suddenness of the attack.

[0016] Furthermore, the data sources collected by the data collection module of the system include publicly available network data obtained legally and test data generated in a simulated environment.

[0017] The beneficial effects of an AI-based intelligent attack system of the present invention are as follows:

[0018] (1) The present invention comprehensively uses web crawler and traffic sniffing technologies to collect multi-dimensional information of the target network, providing a basis for in-depth analysis. A digital twin model is constructed through deep learning algorithms to detect security vulnerabilities and improve the pertinence of attacks.

[0019] (2) Based on the intelligent analysis results, the present invention uses reinforcement learning algorithms to intelligently select attack tools, paths, and sequences according to the actual situation of the target network, generating diverse strategies to ensure the effectiveness of attacks.

[0020] (3) According to the attack strategy calling tool technology, the present invention monitors the attack effect in real time and dynamically adjusts the strategy to ensure the continuous progress of the attack.

[0021] (4) The present invention evaluates the attack effect from multiple dimensions, feeds the results back to relevant modules, optimizes the attack system, and improves the success rate of subsequent attacks.

[0022] (5) The present invention can perceive the changes in the target network and the adjustment of defense measures in real time, automatically learn and dynamically change the attack strategy to avoid the recognition and block of traditional defense systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The following further describes the present invention in detail with reference to the drawings and specific implementation methods.

[0024] Figure 1 It is a schematic structural diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The present invention will be described in detail below with reference to the drawings and embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0026] To make the technical solution of the present invention clearer, the following further describes the present invention in detail with reference to the drawings and specific embodiments.

[0027] Referring to Figure 1 , an AI-based intelligent attack system includes: a data collection module: using web crawler and traffic sniffing technologies to collect the network topology structure, device configuration information, user behavior data, and security policy settings of the target network, providing basic data for subsequent analysis.

[0028] Intelligent Analysis Module: Adopting the graph neural network algorithm in deep learning, it deeply analyzes the collected data, constructs a digital twin model of the target network, simulates the normal operating state and potential changes, and by comparing historical attack data, mines security vulnerabilities and weak links (e.g., by analyzing abnormal fluctuations in network traffic and combining historical attack patterns, quickly locates potential security hazards).

[0029] Attack Strategy Generation Module: Based on the intelligent analysis results, it uses reinforcement learning algorithms to generate optimal attack strategies, including selecting attack tools, determining attack paths, and arranging attack sequences, while generating diversified strategies to cope with defense mechanisms.

[0030] Attack Execution Module: According to the generated attack strategies, it invokes corresponding attack tools and techniques, such as vulnerability scanning tools, malware propagation, DDoS attacks, etc. to launch attacks on the target network, and monitors the attack effects in real-time and dynamically adjusts the strategies.

[0031] Attack Effect Evaluation Module: After the attack is executed, it evaluates the attack effects by analyzing indicators such as the response of the target network, data leakage situation, and service interruption degree, and feeds the results back to the intelligent analysis module and the attack strategy generation module to optimize the attack system.

[0032] Refer to Figure 1 , the system has adaptive attack capabilities, can automatically learn and dynamically change attack strategies and methods according to the real-time changes of the target network and defense measures, and avoid being recognized and blocked by traditional defense systems.

[0033] Refer to Figure 1 , the web crawler will simulate the browsing behavior of normal users, deeply traverse the target network, capture the public network topology structure and device configuration details, and not miss any possible network paths. Traffic sniffing is deployed at key network nodes to analyze the real-time flowing network traffic, obtain user behavior data, including user login time, access frequency, common operations, etc., as well as security policy settings, such as firewall rules, intrusion detection system configurations, etc., to lay a solid data foundation for subsequent in-depth analysis.

[0034] Refer to Figure 1 , the attack strategy generation module will intelligently select attack tools according to the actual situation of the target network, such as dedicated scanning tools for specific vulnerabilities, carefully customized malware, etc. When determining the attack path, it fully considers the network topology structure, avoids high-risk areas, and selects the most concealed and efficient path. At the same time, arrange a reasonable attack sequence, first conduct a tentative attack, and then gradually increase the attack intensity according to the reaction of the target network. Generate diversified strategies for different defense mechanisms to ensure the effectiveness of the attack.

[0035] Refer to Figure 1During the attack process, the attack execution module monitors the attack effect in real time, dynamically adjusts the attack strategy by analyzing the response data, traffic changes, etc. of the target network. Once it is found that the attack is blocked, the attack method is immediately switched to ensure the continuous progress of the attack.

[0036] Refer to Figure 1 The attack effect evaluation module judges the activation situation of its defense mechanism by analyzing the response of the target network; counts the data leakage situation and evaluates the results of the attack in terms of data acquisition; measures the degree of service interruption to determine the impact on the target network service. These evaluation results are timely fed back to the intelligent analysis module and the attack strategy generation module to optimize the attack system and improve the success rate of subsequent attacks.

[0037] Refer to Figure 1 The system uses AI technology to intelligently disguise attack behaviors, making them appear as normal network traffic or user behaviors, reducing the risk of being detected, and increasing the concealment and suddenness of attacks.

[0038] Refer to Figure 1 The data sources collected by the data collection module of the system include publicly available network data obtained legally and test data generated in a simulated environment.

[0039] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. An AI-based intelligent attack system, characterized in that: include: Data collection module: Use web crawlers and traffic sniffing technology to collect the network topology, device configuration information, user behavior data, and security policy settings of the target network to provide basic data for subsequent analysis. Intelligent analysis module: Uses the graph neural network algorithm in deep learning to conduct in-depth analysis of the collected data, builds a digital twin model of the target network, simulates normal operating status and potential changes, and discovers security vulnerabilities and weak links by comparing historical attack data. Attack strategy generation module: Based on the results of intelligent analysis, the reinforcement learning algorithm is used to generate the optimal attack strategy, including selecting attack tools, determining attack paths and arranging attack sequences, while generating diversified strategies to deal with defense mechanisms. Attack execution module: Based on the generated attack strategy, call the corresponding attack tools and technologies, such as vulnerability scanning tools, malware propagation, DDoS attacks, etc. to launch attacks on the target network, monitor the attack effects in real time, and dynamically adjust the strategy. Attack effect evaluation module: After the attack is executed, the attack effect is evaluated by analyzing indicators such as the response of the target network, data leakage, and the degree of service interruption, and the results are fed back to the intelligent analysis module and the attack strategy generation module to optimize the attack system.

2. The AI-based intelligent attack system according to claim 1, characterized in that: The system has adaptive attack capabilities and can automatically learn and dynamically change attack strategies and methods according to real-time changes in the target network and adjustments to defense measures to avoid being identified and blocked by traditional defense systems.

3. The AI-based intelligent attack system according to claim 1, characterized in that: Web crawlers simulate normal user browsing behavior, deeply traverse the target network, capture public network topology and device configuration details, and do not miss any possible network paths. Traffic sniffers are deployed at key network nodes to analyze real-time network traffic and obtain user behavior data, including user login time, access frequency, common operations, etc., as well as security policy settings, such as firewall rules and intrusion detection system configuration, to lay a solid data foundation for subsequent in-depth analysis.

4. The AI-based intelligent attack system according to claim 1, characterized in that: The attack strategy generation module will intelligently select attack tools according to the actual situation of the target network, such as special scanning tools for specific vulnerabilities and carefully customized malware. When determining the attack path, the network topology is fully considered, high-risk areas are avoided, and the most hidden and efficient path is selected. At the same time, a reasonable attack sequence is arranged, and a tentative attack is carried out first, and then the attack intensity is gradually increased according to the response of the target network. Diversified strategies are generated for different defense mechanisms to ensure the effectiveness of the attack.

5. The AI-based intelligent attack system according to claim 1, characterized in that: During the attack process, the attack execution module monitors the attack effect in real time and dynamically adjusts the attack strategy by analyzing the response data and traffic changes of the target network. Once the attack is blocked, the attack mode is immediately switched to ensure that the attack continues.

6. The AI-based intelligent attack system according to claim 1, characterized in that: The attack effect evaluation module determines the activation of the target network's defense mechanism by analyzing the target network's response; it calculates the data leakage and evaluates the attack's results in data acquisition; Measure the degree of service interruption and determine the impact on the target network business. Feedback these evaluation results to the intelligent analysis module and attack strategy generation module in a timely manner to optimize the attack system and improve the success rate of subsequent attacks.

7. The AI-based intelligent attack system according to claim 1, characterized in that: The system uses AI technology to intelligently disguise attack behaviors, making them appear as normal network traffic or user behaviors, reducing the risk of detection and increasing the stealth and suddenness of attacks.

8. The AI-based intelligent attack system according to claim 1, characterized in that: The data sources collected by the data collection module of the system include legally obtained public network data and test data generated in a simulation environment.

Images