Intelligent penetration testing method for power internet of things systems combining ontology knowledge and reinforcement learning

With the application of new-generation information technologies such as big data, artificial intelligence, and the energy Internet in Power Internet of Things (IoT) systems, a large number of IoT terminals, acquisition terminals, and transmission devices have achieved integrated interconnection and comprehensive information interaction. However, this transformation also brings new challenges: the security risk of intrusions into power IoT systems has significantly increased, making the assurance of power system information security a research hotspot. Penetration testing, as an essential means of information security protection, is critical for identifying and fixing security vulnerabilities. Given the complexity of power IoT systems and the limitations of traditional manual testing methods, this paper proposes an automated penetration testing method that combines prior knowledge with deep reinforcement learning. It aims to intelligently explore optimal attack paths under conditions where the system state is unknown. By constructing an ontology knowledge model to fully utilize prior knowledge and introducing an attention mechanism to address the issue of varying state spaces, the efficiency of penetration testing can be improved. Experimental results show that the proposed method effectively optimizes path decision-making for penetration testing, providing support for the security protection of power IoT systems.