Self-adaptive penetration testing method and system based on reinforcement learning

Abstract

The invention relates to the technical field of network security, in particular to an adaptive penetration test method and system based on reinforcement learning, and the method comprises the steps: constructing a state space model, defining an action space, designing a reward function, optimizing a strategy model, and analyzing a test result. According to the method, a reinforcement learning mechanism is combined with target environment feedback, so that multi-step attack path planning and adaptive strategy adjustment are realized, and the intelligence and coverage depth of penetration testing are remarkably improved. And meanwhile, through backtracking analysis of the state transition sequence, the potential threat path is comprehensively identified, and accurate guidance is provided for security protection.

Description

Technical Field

[0001] This invention belongs to the field of network security technology, specifically an adaptive penetration testing method and system based on reinforcement learning. Background Technology

[0002] With the rapid development of information technology, cybersecurity threats are becoming increasingly severe. Penetration testing, as an effective means of proactively discovering system security vulnerabilities, plays a crucial role in ensuring cyberspace security. Traditional penetration testing typically relies on a combination of human experience and automated tools. Human penetration testing demonstrates strong adaptability in complex scenarios, but its efficiency is low, and it requires high levels of skill and experience from testers. Existing automated penetration testing tools, such as vulnerability scanners and scripted penetration frameworks, primarily rely on preset rules and known vulnerability databases for scanning and attack attempts. These tools have limitations when facing unknown vulnerabilities, complex attack chains, or dynamically changing target environments, lacking a deep understanding of the target environment and the ability to adaptively adjust attack strategies. Furthermore, they struggle to simulate the thought processes of human penetration testing experts, have limited ability to explore multi-step attack paths, and lack intelligent evaluation and learning mechanisms for attack results, leading to incomplete test coverage. Significant human intervention is still required to compensate for these deficiencies, impacting the overall efficiency and depth of penetration testing. Therefore, improving the intelligence, automation, and adaptability of penetration testing, enabling it to perceive the environment, plan attacks, and learn from feedback, has become an urgent technical challenge. Summary of the Invention

[0003] This invention provides an adaptive penetration testing method and system based on reinforcement learning, addressing the limitations of existing automated penetration testing tools when facing unknown vulnerabilities, complex attack chains, or dynamically changing target environments. This method introduces a reinforcement learning mechanism, combining real-time feedback from the target environment with multi-step attack path planning capabilities, to simulate the thought process of human penetration testing experts, thereby significantly improving the intelligence, automation, and adaptability of penetration testing.

[0004] The adaptive penetration testing method based on reinforcement learning provided by this invention includes the following steps: First, an initial state space model is constructed. This model generates a set of state vectors by scanning and analyzing the topology, service configuration, and known vulnerability information of the target network environment. Each state vector corresponds to a possible state in the target environment. Second, an action space is defined, which consists of a series of executable penetration testing operations, including but not limited to port scanning, vulnerability exploitation attempts, privilege escalation, and lateral movement. Each operation is mapped to a discrete action in the action space. Next, a reward function is designed. This function quantifies and evaluates the impact on the target environment during the penetration test. Specifically, it includes positive rewards for successfully discovering vulnerabilities, obtaining sensitive data, or escalating privileges, as well as negative penalties for triggering security protection mechanisms or causing system anomalies. Then, based on the reinforcement learning algorithm, the target environment is interactively explored through a simulator, and the strategy model is gradually optimized so that the strategy can select the optimal action in different states to maximize the cumulative reward. Finally, the optimized strategy is applied to the actual target environment, the state transition sequence generated during the test is recorded, and potential security threat paths are extracted through backtracking analysis to form the final penetration test report.

[0005] Furthermore, this invention also provides an adaptive penetration testing system based on reinforcement learning, which includes a state modeling unit, an action definition unit, a reward calculation unit, a policy optimization unit, and a result analysis unit. The state modeling unit is responsible for scanning and analyzing the target network environment and generating an initial state space model; the action definition unit is responsible for defining the action space and mapping it to specific penetration testing operations; the reward calculation unit quantifies and evaluates the behavior during the penetration testing process based on preset rules and generates reward signals; the policy optimization unit trains and optimizes the policy model using reinforcement learning algorithms to enable it to adapt to the dynamically changing target environment; and the result analysis unit performs backtracking analysis on the state transition sequences generated during the testing process, identifies potential security threat paths, and generates a penetration testing report.

[0006] This invention offers at least the following technical advantages: By introducing a reinforcement learning mechanism, it addresses the problem of existing automated penetration testing tools lacking a deep understanding of the target environment, enabling the testing tools to adaptively adjust attack strategies in dynamically changing environments. Simultaneously, by simulating the thought process of human penetration testing experts, this invention achieves effective exploration of multi-step attack paths, significantly improving the coverage and depth of penetration testing. Furthermore, by backtracking and analyzing the state transition sequences generated during the testing process, this invention can comprehensively identify potential security threat paths, providing more precise guidance for network security protection.

[0007] In the implementation process, the state modeling unit uses network scanning and vulnerability detection tools to collect and organize the topology, service configuration, and known vulnerability information of the target environment, generating a set of state vectors. Each state vector contains multiple feature values, such as host IP address, open port number, running service type, and whether any known vulnerabilities exist. These feature values ​​are converted into numerical form using feature encoding technology to facilitate subsequent processing and analysis.

[0008] The action definition unit designs a standardized set of actions based on the characteristics of the target environment and the needs of penetration testing. Each action is explicitly mapped to a specific penetration testing operation; for example, a port scanning operation corresponds to sending a specific type of network probe packet, and an exploit attempt operation corresponds to executing a specific exploit script. To improve the flexibility of the action space, the action definition unit also supports dynamic expansion, allowing users to add new action types according to actual needs.

[0009] The design of the reward calculation unit is one of the core elements of this invention. This unit quantifies and evaluates the behavior during the penetration testing process using preset rules, generating reward signals. Specifically, when the testing tool successfully discovers a vulnerability in the target environment or obtains sensitive data, the reward calculation unit allocates a corresponding positive reward value based on the severity of the vulnerability or the importance of the data; when the testing tool triggers the target environment's security protection mechanisms or causes system anomalies, the reward calculation unit allocates a negative penalty value. This reward mechanism not only guides the testing tool to prioritize high-value operations but also prevents it from executing behaviors that could lead to serious consequences.

[0010] The policy optimization unit employs deep reinforcement learning algorithms to train and optimize the policy model. This unit interactively explores the target environment through a simulator, gradually accumulating experience data and using this data to update the policy model's parameters. During training, the policy optimization unit uses experience replay technology, storing historical experience data in an experience pool and randomly sampling a batch of data from it for model updates, thereby improving training stability and efficiency. Furthermore, the policy optimization unit introduces an exploration-utilization balance mechanism, dynamically adjusting exploration probabilities to ensure the policy model achieves a balance between exploring new actions and utilizing existing experience.

[0011] The results analysis unit identifies potential security threat paths by backtracking the state transition sequences generated during the testing process. Specifically, this unit first performs time series analysis on the state transition sequences to extract key state transition points; then, based on these key points, it constructs a threat path diagram to visually display the security threats discovered during the penetration test; finally, it generates a penetration test report based on the threat path diagram, detailing the security vulnerabilities discovered during the test and their potential impact.

[0012] In summary, this invention significantly enhances the intelligence, automation, and adaptability of penetration testing by introducing a reinforcement learning mechanism and combining real-time feedback from the target environment with multi-step attack path planning capabilities. Furthermore, by backtracking and analyzing the state transition sequences generated during the testing process, this invention can comprehensively identify potential security threat paths, providing more precise guidance for network security protection. Attached Figure Description

[0013] Figure 1 The flowchart of the adaptive penetration testing method based on reinforcement learning provided in the embodiments of the present invention illustrates the overall steps from state modeling to result analysis; Figure 2 This is a schematic diagram illustrating the working principle of the strategy optimization unit in this embodiment of the invention, describing the process of interacting with the target environment through a simulator and optimizing the strategy model; Figure 3 This is a schematic diagram of the threat path graph generated by the result analysis unit in this embodiment of the invention, illustrating how to extract key points from the state transition sequence and construct a security threat path; The attached figures are labeled as follows: 1. State modeling unit; 2. Action definition unit; 3. Reward calculation unit; 4. Strategy optimization unit; 5. Result analysis unit; 6. Simulator; 7. Target environment; 8. State transition sequence; 9. Threat path diagram. Detailed Implementation

[0014] This invention provides an adaptive penetration testing method and system based on reinforcement learning, the specific implementation of which is as follows. (See attached diagram.) Figure 1 To be continued Figure 3 The accompanying drawings, along with the component numbers marked in the figures, provide a detailed description of the specific implementation process of this invention.

[0015] In practical applications, the system of this invention includes a state modeling unit 1, an action definition unit 2, a reward calculation unit 3, a strategy optimization unit 4, and a result analysis unit 5. These units are closely connected through data flow and logical relationships to jointly complete the penetration testing task. The target environment 7 is the external interaction object of the system, and the simulator 6, as an important component of the strategy optimization unit 4, is used to interact with the target environment 7 and generate experience data during the training phase. The state transition sequence 8 is the key data generated during the testing process, and the threat path graph 9 is the core output content generated by the result analysis unit 5.

[0016] State modeling unit 1 is responsible for scanning and analyzing the target network environment to generate an initial state space model. Specifically, state modeling unit 1 uses common network scanning tools such as Nmap or vulnerability detection tools such as OpenVAS to collect topology information, service configuration information, and known vulnerability information of the target environment 7. This information is organized into a set of state vectors, each containing multiple feature values, such as host IP address, open port number, running service type, and whether a known vulnerability exists. To facilitate subsequent processing, these feature values ​​are converted into numerical form using feature encoding technology, forming a high-dimensional vector set. State modeling unit 1 then passes the generated state vectors to policy optimization unit 4 as input data for the reinforcement learning algorithm.

[0017] Action definition unit 2 designs a standardized set of actions based on the characteristics of target environment 7 and the needs of penetration testing, and maps them to specific penetration testing operations. For example, a port scanning operation corresponds to sending a specific type of network probe packet, an exploit attempt operation corresponds to executing a specific exploit script, and a privilege escalation operation corresponds to a sequence of commands to attempt to gain higher privileges. Action definition unit 2 supports dynamic expansion, allowing users to add new action types according to actual needs. Action definition unit 2 passes the generated action set to policy optimization unit 4 as the action space for reinforcement learning algorithms.

[0018] The reward calculation unit 3 quantifies and evaluates the behavior during the penetration testing process based on preset rules, generating reward signals. The design of reward calculation unit 3 needs to incorporate feedback information from the target environment 7. Specifically, when the testing tool successfully discovers a vulnerability in the target environment 7 or obtains sensitive data, reward calculation unit 3 allocates a corresponding positive reward value based on the severity of the vulnerability or the importance of the data. For example, successfully exploiting a high-risk vulnerability may yield a higher positive reward value, while discovering a low-risk vulnerability yields a lower positive reward value. When the testing tool triggers the security protection mechanism of the target environment 7 or causes system anomalies, reward calculation unit 3 allocates a negative penalty value. This reward mechanism design guides the testing tool to prioritize high-value operations while avoiding actions that could lead to serious consequences. Reward calculation unit 3 transmits the generated reward signals to policy optimization unit 4 for updating the policy model.

[0019] Policy optimization unit 4 uses a deep reinforcement learning algorithm to train and optimize the policy model. Its working principle is shown in the appendix. Figure 2As shown, the policy optimization unit 4 interactively explores the target environment 7 through the simulator 6, gradually accumulating experience data and using this data to update the parameters of the policy model. During training, the policy optimization unit 4 first selects an action from the current state space, which is determined by the policy model. The simulator 6 applies this action to the target environment 7 and returns the next state and the corresponding reward value. The policy optimization unit 4 stores the state transition information in the experience pool and randomly samples a batch of data from it for model updates. To improve the stability and efficiency of training, the policy optimization unit 4 introduces an experience replay technique to ensure that historical experience data can be fully reused. In addition, the policy optimization unit 4 also introduces an exploration-utilization balance mechanism, which dynamically adjusts the exploration probability to ensure that the policy model can achieve a balance between exploring new actions and utilizing existing experience. After multiple iterations of training, the policy optimization unit 4 finally generates an optimized policy model that can select the optimal action based on the current state to maximize the cumulative reward.

[0020] The results analysis unit 5 identifies potential security threat paths by performing backtracking analysis on the state transition sequence 8 generated during the testing process. Specifically, the results analysis unit 5 first performs time series analysis on the state transition sequence 8 to extract key state transition points. These key points typically correspond to significant events occurring during the penetration test, such as successfully exploiting vulnerabilities or obtaining sensitive data. Next, the results analysis unit 5 constructs a threat path diagram 9 based on these key points, visually displaying the security threats discovered during the penetration test. The generation process of threat path diagram 9 is shown in the attached figure. Figure 3 As shown, its core lies in connecting the key points in state transition sequence 8 in chronological order to form one or more complete attack paths. Finally, the result analysis unit 5, combined with the threat path diagram 9, generates a penetration test report, detailing the security vulnerabilities discovered during the test and their potential impact. The penetration test report not only includes detailed information about the vulnerabilities but also provides remediation recommendations for each vulnerability, offering precise guidance for network security protection.

[0021] In a specific application scenario, assume the target environment 7 is an enterprise intranet containing multiple servers and terminal devices. State modeling unit 1 obtains the intranet's topology information, such as server IP addresses, open ports, and service types, using scanning tools. Action definition unit 2 designs a set of actions based on the intranet's characteristics, including port scanning, vulnerability exploitation attempts, and lateral movement. Reward calculation unit 3 sets reward rules based on the intranet's security policy; for example, successfully exploiting a high-risk vulnerability yields a high positive reward, while triggering a firewall alert results in a negative penalty. Policy optimization unit 4 interacts with the intranet through simulator 6 to progressively optimize the policy model. Finally, result analysis unit 5 performs backtracking analysis on the state transition sequence 8, generates a threat path diagram 9, and produces a penetration test report, detailing the security threats present in the intranet and their potential impact.

[0022] The above embodiments illustrate the specific implementation process of the present invention. Through close collaboration among various units, an adaptive penetration testing method and system based on reinforcement learning is realized. The present invention can adaptively adjust attack strategies in dynamically changing target environments, significantly improving the intelligence, automation, and adaptability of penetration testing. Furthermore, by backtracking and analyzing the state transition sequences generated during the testing process, it can comprehensively identify potential security threat paths, providing more precise guidance for network security protection.

[0023] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0024] Assume target environment 7 is the internal network system of a large enterprise, containing multiple servers, terminal devices, and security protection facilities such as firewalls. These devices are connected through a complex network topology and run various services to support the enterprise's daily operations. In this scenario, the reinforcement learning-based adaptive penetration testing method and system of this invention will proceed according to the following steps, while combining with the attached... Figure 1 To be continued Figure 3 The component numbers in the document will be explained in detail.

[0025] First, the state modeling unit 1 performs a comprehensive scan of the target environment 7 by invoking network scanning tools such as Nmap and vulnerability detection tools such as OpenVAS. During this process, the state modeling unit 1 collects topology information about the target environment 7, such as the IP addresses of each server, open port numbers, types of services running, and the presence of known vulnerabilities. This information is organized into a set of state vectors, each containing multiple feature values, such as host identifiers, port status, and service configuration information. To facilitate subsequent processing, these feature values ​​are converted into numerical forms using feature encoding techniques, forming a high-dimensional vector set. The state modeling unit 1 then passes the generated state vectors to the policy optimization unit 4 as initial input data for the reinforcement learning algorithm. The core of this step is to ensure that subsequent action selections are based on accurate environmental information through comprehensive modeling of the target environment 7.

[0026] Next, Action Definition Unit 2 designs a standardized set of actions based on the characteristics of the target environment 7 and the needs of penetration testing. These actions include, but are not limited to, port scanning, exploit attempts, privilege escalation, and lateral movement. For example, a port scanning operation corresponds to sending specific types of network probe packets, an exploit attempt operation corresponds to executing a predefined exploit script, and a privilege escalation operation corresponds to attempting to acquire higher privileges using a sequence of commands. Action Definition Unit 2 also supports dynamic expansion, allowing users to add new action types according to actual needs. Action Definition Unit 2 passes the generated action set to Policy Optimization Unit 4 as the action space for the reinforcement learning algorithm. The key to this step is ensuring that the system can adapt to the penetration testing needs of different scenarios through a flexible action definition mechanism.

[0027] Subsequently, the reward calculation unit 3 quantifies and evaluates the behavior during the penetration testing process based on preset rules, generating reward signals. For example, when the testing tool successfully discovers a high-risk vulnerability in the target environment 7, the reward calculation unit 3 allocates a higher positive reward value; conversely, when the testing tool triggers the security protection mechanism of the target environment 7 or causes system anomalies, the reward calculation unit 3 allocates a negative penalty value. This reward mechanism not only guides the testing tool to prioritize high-value operations but also prevents it from executing behaviors that could lead to serious consequences. The reward calculation unit 3 transmits the generated reward signals to the strategy optimization unit 4 to update the strategy model. The significance of this step lies in driving the testing tool to achieve a balance between exploration and exploitation through reasonable reward design, thereby gradually optimizing the strategy model.

[0028] In policy optimization unit 4, a deep reinforcement learning algorithm is introduced to train and optimize the policy model. (See attached...) Figure 2As shown, the policy optimization unit 4 interactively explores the target environment 7 through the simulator 6, gradually accumulating experience data. During training, the policy optimization unit 4 first selects an action from the current state space, which is determined by the policy model. The simulator 6 applies this action to the target environment 7 and returns the next state and the corresponding reward value. The policy optimization unit 4 stores the state transition information in the experience pool and randomly samples a batch of data from it for model updates. To improve the stability and efficiency of training, the policy optimization unit 4 introduces an experience replay technique to ensure that historical experience data can be fully reused. In addition, the policy optimization unit 4 also introduces an exploration-exploitation balance mechanism, which dynamically adjusts the exploration probability to ensure that the policy model can achieve a balance between exploring new actions and utilizing existing experience. After multiple iterations of training, the policy optimization unit 4 finally generates an optimized policy model that can select the optimal action based on the current state to maximize the cumulative reward. The core of this step lies in the continuous optimization of the reinforcement learning algorithm, enabling the system to adaptively adjust the attack strategy in a dynamically changing target environment.

[0029] Finally, the results analysis unit 5 identifies potential security threat paths by performing backtracking analysis on the state transition sequence 8 generated during the test. (See attached...) Figure 3 As shown, the results analysis unit 5 first performs time-series analysis on the state transition sequence 8 to extract key state transition points. These key points typically correspond to major events occurring during the penetration test, such as successfully exploiting vulnerabilities or obtaining sensitive data. Next, the results analysis unit 5 constructs a threat path diagram 9 based on these key points, visually displaying the security threats discovered during the penetration test. The core of generating the threat path diagram 9 lies in connecting the key points in the state transition sequence 8 in chronological order to form one or more complete attack paths. Finally, the results analysis unit 5 combines the threat path diagram 9 to generate a penetration test report, detailing the security vulnerabilities discovered during the test and their potential impact. The penetration test report includes not only detailed information about the vulnerabilities but also remediation recommendations for each vulnerability, providing precise guidance for network security protection. The significance of this step lies in the ability to comprehensively identify potential security threat paths through retrospective analysis of the state transition sequences generated during the test, providing a basis for subsequent security protection work.

[0030] In summary, in the specific application scenarios described above, the system of this invention, through the close collaboration of the state modeling unit 1, action definition unit 2, reward calculation unit 3, strategy optimization unit 4, and result analysis unit 5, realizes an adaptive penetration testing method and system based on reinforcement learning. This invention can adaptively adjust attack strategies in dynamically changing target environments, significantly improving the intelligence, automation, and adaptability of penetration testing. Furthermore, by backtracking and analyzing the state transition sequences generated during the testing process, it can comprehensively identify potential security threat paths, providing more precise guidance for network security protection.

Claims

1. An adaptive penetration testing method based on reinforcement learning, characterized in that, The method includes the following steps: An initial state space model is constructed. By scanning and analyzing the topology, service configuration, and known vulnerability information of the target network environment, a set of state vectors is generated, with each state vector corresponding to a possible state in the target environment. Define an action space, which consists of a series of executable penetration testing operations, each of which is mapped to a discrete action in the action space; Design a reward function to quantitatively evaluate the impact on the target environment during penetration testing and generate reward signals; Based on reinforcement learning algorithms, the system interacts with the target environment through a simulator to gradually optimize the policy model, enabling the policy to select the optimal action in different states to maximize the cumulative reward. The optimized strategy is applied to the actual target environment, the state transition sequence generated during the test is recorded, and potential security threat paths are extracted through backtracking analysis to form the final penetration test report.

2. The method according to claim 1, characterized in that, Constructing the initial state-space model includes the following steps: Use network scanning and vulnerability detection tools to collect topology information, service configuration information, and known vulnerability information of the target environment; The collected information is organized into a set of state vectors. Each state vector contains multiple feature values, including host IP address, open port number, running service type, and whether there are known vulnerabilities. Feature values ​​are converted into numerical forms using feature encoding techniques.

3. The method according to claim 1, characterized in that, Defining the action space includes the following steps: Design a standardized set of actions based on the characteristics of the target environment and the requirements of penetration testing; Each action is explicitly mapped to a specific penetration testing operation, including port scanning, exploit attempts, privilege escalation, and lateral movement. It supports dynamic expansion, allowing users to add new action types according to actual needs.

4. The method according to claim 1, characterized in that, Designing a reward function involves the following steps: When the testing tool successfully discovers vulnerabilities in the target environment or obtains sensitive data, a positive reward value is assigned based on the severity of the vulnerability or the importance of the data. When the testing tool triggers the security protection mechanism of the target environment or causes system abnormalities, a negative penalty value is assigned.

5. The method according to claim 1, characterized in that, The optimization of the policy model based on reinforcement learning algorithms includes the following steps: Select an action from the current state space, which is determined by the policy model; The simulator applies the action to the target environment and returns the next state and the corresponding reward value. The state transition information is stored in an experience pool, and a batch of data is randomly sampled from it for model updates; By introducing experience replay techniques and an exploration-balance mechanism, we ensure that the strategy model can achieve a balance between exploring new actions and utilizing existing experience.

6. The method according to claim 1, characterized in that, Extracting potential security threat paths through backtracking analysis includes the following steps: Perform time series analysis on the state transition sequence to extract key state transition points; A threat path map is built based on key points to intuitively display the security threats discovered during penetration testing. Generate a penetration test report by combining the threat path map, and describe in detail the security vulnerabilities discovered during the test and their potential impact.

7. An adaptive penetration testing system based on reinforcement learning, characterized in that, The system includes: State modeling unit (1) is used to scan and analyze the target network environment and generate an initial state space model; Action definition unit (2) is used to define the action space and map it to specific penetration testing operations; The reward calculation unit (3) is used to quantify and evaluate the behavior during the penetration test based on preset rules and generate reward signals; The policy optimization unit (4) is used to train and optimize the policy model using a reinforcement learning algorithm; The results analysis unit (5) is used to perform backtracking analysis on the state transition sequence generated during the test, identify potential security threat paths, and generate a penetration test report.

8. The system according to claim 7, characterized in that, The state modeling unit (1) collects the topology information, service configuration information and known vulnerability information of the target environment by calling network scanning tools and vulnerability detection tools, and organizes the collected information into a set of state vectors. Each state vector contains multiple feature values, including host IP address, open port number, running service type and whether there are known vulnerabilities.

9. The system according to claim 7, characterized in that, The strategy optimization unit (4) interacts with the target environment (7) through the simulator (6), gradually accumulates experience data, and uses this data to update the parameters of the strategy model. At the same time, it introduces experience replay technology and exploration-utilization balancing mechanism.

10. The system according to claim 7, characterized in that, The results analysis unit (5) performs time series analysis on the state transition sequence (8), extracts key state transition points, constructs a threat path map (9) based on the key points, and generates a penetration test report by combining the threat path map.

Images