Federated Learning for Intrusion Detection Systems in Medical Cyber-Physical Systems

Abstract

Medical Cyber-Physical Systems (MCPS) are networked systems of medical devices with seamless integration of physical and computation components. MCPS are increasingly used in healthcare environments to deliver high quality care by enabling continuous monitoring and treatment. However, security breaches can compromise privacy, integrity, and availability for medical devices while circumventing traditional approaches such as cryptography. This can lead to severe repercussions for both the patient and hospital in terms of injury and liability. We implement a massively distributed, machine-learning-based IDS for MCPS based on Federated Learning -- FLIDS. We evaluate our design with real patient data and against Denial of Service (DoS), data modification, and data injection attacks. Our approach transmits 3.8 times fewer bytes than collecting the data at a central location which saves bandwidth. We also achieve a detection accuracy of greater than 99.0\% and a False Positive Rate (FPR) of 1.0\%. Lastly, we show that FLIDS can cope with unevenly distributed data and is a scalable solution that leverages the computing resources of many mobile devices.