Optimizing IoT Cross-rule Vulnerability Detection through Reinforcement Learning-Based Fuzzing

Internet of Things (IoT) devices have become increasingly ubiquitous and essential to daily life. These devices are usually controlled based on trigger-action rules, meaning that the devices will take actions according to the rules when trigger conditions are satisfied. As more devices are deployed in smart home systems, the risk of undesirable interactions and cross-rule vulnerabilities increases. In this paper, we propose a reinforcement learning-based fuzzing approach that can automate the modification of environmental variables to generate test cases and increase the likelihood of discovering cross-rule conflicts in smart home systems. Our approach optimizes conflict detection and discovers hidden conditions that lead to vulnerabilities. The preliminary results show that our model can successfully recognize different types of rule conflict.