基于改进深度强化学习的电力物联网自动渗透测试技术研究OA

[Purposes]With the comprehensive application of the ubiquitous power internet of things,uncontrollable factors and changes in the physical contact environment have made power inter-net of things systems face severe information security issues.Penetration testing,as an important means of information security protection,can discover and fix security vulnerabilities in advance,ef-fectively reducing the risk of system intrusion.Current penetration testing mainly relies on manual testing,which requires high technical expertise and experience from personnel and has low testing effi-ciency.To address these,an automatic penetration testing method based on improved deep reinforce-ment learning is proposed.[Methods]First,the state space was established for the learning processes according to expert experience and an attention mechanism was introduced to solve the problem of dy-namic changes in the state space.Then,a deep reinforcement learning model was used for automatic exploration of attack penetration paths.Finally,an experimental simulation environment was set up for comparative testing.[Results]The results show that the proposed method exhibits improved con-vergence performance across different networks.