An Intelligent Penetration Test Simulation Environment Construction Method Incorporating Social Engineering Factors

Abstract

The penetration test has many repetitive operations and requires advanced expert knowledge, therefore, the manual penetration test is inefficient. With the development of reinforcement learning, the intelligent penetration test has been a research hotspot. However, the existing intelligent penetration test simulation environments only focus on the exploits of target hosts by the penetration tester agent’s actions while ignoring the important role of social engineering in the penetration test in reality. In addition, the construction of the existing penetration test simulation environment is based on the traditional network graph model without integrating security factors and attributes, and it is difficult to express the interaction between the penetration tester and the target network. This paper constructs an improved network graph model for penetration test (NMPT), which integrates the relevant security attributes of the penetration test. The NMPT model lays the foundation for extending the penetration tester’s social engineering actions. Then, we propose an intelligent penetration test method that incorporates social engineering factors (SE-AIPT) based on the Markov Decision Process. We adopt several mainstream reinforcement learning algorithms to train attack agents. The experiments show that the SE-AIPT method could vividly model the penetration tester agent’s social engineering actions, which effectively improves the reality of the simulation environment. Moreover, the penetration tester agent shows superior effects in the attack path discovery in the intelligent penetration test simulation environment constructed by the SE-AIPT method.

Penetration Testing Framework using the Q Learning Ensemble Deep CNN Discriminator Framework

“…However, it demanded significant computational resources and expertise in machine learning. Yang Li et al [9] introduced an enhanced network graph model for penetration testing, which seamlessly integrated pertinent security attributes into the process. This intelligent penetration testing method leveraged reinforcement learning and social engineering factors.…”

“…They enhance response by swiftly isolating threats, and minimizing damage. This automation accelerates testing, enabling security professionals to focus on strategic analysis [9]. By enhancing threat detection accuracy, optimizing resource allocation, and reducing false positives, ML and AI elevate penetration testing's efficiency and effectiveness, fortifying cyber security in an increasingly intricate threat landscape.…”

Artificial intelligence for system security assurance: A systematic literature review

“…AI-driven penetration testing tools, which can adjust their strategies based on real-time data, can simulate complex cyber-attack scenarios more dynamically and realistically than traditional methods [120]. Dynamic Penetration Testing [45], [44], [43], [140] Penetration Testing Optimization [103], [57], [64], [30], [178], [142], [71], [78] Penetration Testing in Large-Scale Network [101], [190], [47], [100] Fuzz Testing Fuzz Data Generation [104], [127], [148], [198] Fuzz Testing Performance Improvement…”

“…Incorporating social engineering elements into penetration testing frameworks signi cantly enhances their realism and overall effectiveness. By leveraging RL algorithms within modi ed network graph models, these approaches train and evaluate penetration testing strategies that include tactics like phishing and pretexting, providing a dynamic and realistic setting for cybersecurity testing (Article [103]).…”

Adversarial Decision-Making for Moving Target Defense: A Multi-Agent Markov Game and Reinforcement Learning Approach

“…Chen et al [19] introduce expert knowledge to guide the attack agent to make better decisions in RL-based automated attacks. Li et al [20] construct an improved network graph model and incorporates social engineering into automated penetration testing (PT) platforms. The intelligent attack simulators [2][3][4][5][6] can construct the cyber attack model, utilize attack loads, and plan attack paths automatically, aiming at improving the intelligent level of cyber attack capability.…”