Fed-Inforce-Fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks

Internet of Medical Things (IoMT) has emerged as a combination of sensors, healthcare devices, and Internet of Things (IoT) to deliver better and intelligent healthcare services. However, security and privacy protection issues have hindered its wide integration resulting in lack of quality IoMT dataset. Federated Learning (FL) is an emerging distributed learning method that can be helpful for IoMT networks and smart healthcare systems (SHS) due to enhanced security and privacy. The use of intrusion detection systems (IDS) has become vital for contemporary critical networks to protect against evolving cyber-attacks. Existing security models are often challenging and computationally expensive to apply to resource-limited medical IoT devices. To this end, this research work proposes a privacy-preserving FL-based IDS model named Fed-Inforce-Fusion to identify cyber-attacks from IoMT networks. Specifically, the proposed Fed-Inforce-Fusion model uses reinforcement learning technique to learn the latent relationships of medical data. Next, a FL-based system is designed allowing distributed SHS nodes to collaboratively train a comprehensive IDS model in a privacy preserving manner. Then, a fusion/aggregation strategy is adopted to improve the performance of detection model and reduce communication overhead by allowing participating clients to involve in the federation process dynamically. Theoretical study and experiment analysis show that the proposed Fed-Inforce-Fusion is better and effective in detecting complex attack vectors in comparison to existing benchmark IDS methods. Thus, proposing its efficacy and suitability in real-world IoMT networks.