Research on intrusion detection method for edge networks based on federated reinforcement learning

With the rapid proliferation of Internet of things (IoT) devices, the frequency and intensity of attacks targeting these devices are constantly increasing. Therefore, it's quite important that security mechanisms are continuously updated to ensure the safety of IoT devices. However, as public awareness of privacy grows, many datasets are no longer shared, leading to the emergence of data silos, which hinders the improvement of IoT security. To address this issue, a federated reinforcement learning-based intrusion detection method was proposed, and experiments were conducted using two datasets from the Internet of medical things (IoMT) and Internet of vehicles (IoV) scenarios. Imbalanced traffic sample distributions were designed for each edge agent to simulate a real-world environment, allowing for the evaluation of the detection accuracy and robustness of the global model. Double deep Q-network (DDQN) was employed as the reinforcement learning framework for the edge agents, and the experimental results were evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that the proposed method exhibits strong robustness and detection accuracy.