随着物联网(IoT,Internet of things)设备的迅速普及,针对IoT设备的攻击频率和强度不断上升,因而持续更新安全机制以保障物联网设备的安全显得尤为重要.然而,随着公众隐私意识的增强,越来越多的数据集不再对外共享,形成数据"孤岛"现象,阻碍了物联网安全防护能力的提升.为了解决这一问题,提出了一种基于联邦强化学习的入侵检测方法,并通过医疗物联网(IoMT,Internet of medical things)和车联网(IoV,Internet of vehicles)场景下的两个数据集进行实验验证.为模拟真实环境,在每个边缘代理中设计了不平衡的流量样本分布,进而评估全局模型的检测精度和鲁棒性.采用双深度Q网络(DDQN,double deep Q-network)为边缘代理的强化学习框架,并通过准确率、精确率、召回率和F1 分数对实验结果进行评估.实验结果表明,提出的方法具有良好的鲁棒性和检测精度.
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 datas-ets from the Internet of medical things(IoMT)and Internet of vehicles(IoV)scenarios.Imbalanced traffic sample distri-butions were designed for each edge agent to simulate a real-world environment,allowing for the evaluation of the detec-tion 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.
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