Penetration testing, a crucial industrial practice for securing networked systems and infrastructures, has traditionally depended on the extensive expertise of human professionals. Addressing the scarcity of human experts, the development of automated penetration testing tools emerges as a promising avenue. Against the backdrop of rapid advancements in artificial intelligence technologies, reinforcement learning has demonstrated considerable potential for realizing automated penetration testing. However, existing research predominantly concentrates on reinforcement learning-based automated penetration testing tools within static scenarios, with limited exploration in dynamic network environments. This paper addresses a noteworthy challenge in developing autonomous agents for real-world applications, particularly focusing on scenarios marked by environmental changes. Such alterations necessitate autonomous agents to continuously monitor environmental characteristics, and adapt, and adjust learned actions to ensure the system’s effective operation. Consequently, the paper proposes an automated reinforcement learning-based penetration testing scheme tailored for dynamic network scenarios, named DynPen. DynPen captures observed changes in the scenario, aiding the penetration testing agent in decision-making based on historical experiences. Simulation results demonstrate the proposed scheme’s efficacy in significantly expediting the convergence speed of the penetration testing agent using reinforcement learning algorithms. Furthermore, the scheme successfully maintains the learning agility and adaptability of the agent in dynamic network scenarios.

Internet users have suffered from phishing attacks for a long time. Attackers deceive users through malicious constructed phishing websites to steal sensitive information, such as bank account numbers, website usernames, and passwords. In recent years, many phishing detection solutions have been proposed, which mainly leverage whitelists or blacklists, website content, or side channel-based techniques. However, with the continuous improvement of phishing technology, current methods have difficulty in achieving effective detection. Hence, in this paper, we propose an effective phishing website detection approach, which we call HinPhish. HinPhish extracts various link relationships from webpages and uses domains and resource objects to construct a heterogeneous information network. HinPhish applies a modified algorithm to leverage the characteristics of different link types in order to calculate the phish-score of the target domain on the webpage. Moreover, HinPhish not only improves the accuracy of detection, but also can increase the phishing cost for attackers. Extensive experimental results demonstrate that HinPhish can achieve an accuracy of 0.9856 and F1-score of 0.9858.

Penetration testing is widely acknowledged as the foremost method for evaluating network security. However, three challenges impede the generation of strategies that align with human expectations. In this article, we present, for the first time, a method based on human feedback to enhance strategy generation. Our approach comprises two components: agent training and decision-making. During agent training, we establish a hierarchical framework to decompose tasks and a knowledge base to offer advice for improving data efficiency. We then impose constraints on the action space to mitigate ineffective exploration. Finally, we train a reward model based on human feedback and fine tune the model guided by this reward model. In decision-making, we process the model output to enhance decision accuracy. We crafted scenarios based on real-world networks, and the results demonstrate the effectiveness of our method in generating penetration testing strategies that align more closely with human intentions.