基于软件机器人的工控靶场应用软件行为模拟

The cyber security range in the industrial control field provides important support for studies on industrial control system (ICS) security. The application behavior simulation is a crucial task for the cyber security range in the industrial control field. Therefore, a software robot method was proposed to realistically simulate the application behavior for the cyber security range in the industrial control field. By considering software graphical interfaces and explicit and implicit software rules, a software menu acquisition algorithm based on scale invariant feature transform (SIFT) for image similarity, as well as a hybrid hierarchical state machine model was developed to model application behaviors. In view of the intelligent problem of the software robot, a deep Q network (DQN) algorithm was utilized to drive the software robot to autonomously learn the application behavior. At the same time, the DQN algorithm was optimized by combining multiple experience replays and multiple target networks. The experiment results show that the software robot based on DQN can effectively learn the industrial control software, and the optimized DQN algorithm has a better autonomous learning effect.