Machine-Learning Classifiers for Security in Connected Medical Devices

Medical devices equipped with wireless connectivity and remote monitoring features are increasingly becoming connected to each other, to an outside programmer and even to the Internet. While Internet of Things technology enables health-care professionals to fine tune or modify medical device settings without invasive procedures, this also opens up large attack surfaces and introduces potential security vulnerabilities. Medical device hacks are slowly becoming a reality and it becomes more critical than ever to defend and protect these devices from security attacks. In this paper, we assess the feasibility of using machine learning models to efficiently determine attacks targeted on a medical device. Specifically, we develop feature sets to accurately profile a medical device and observe any deviation from its normal behavior. We test our method using different machine learning algorithms and provide a comparison analysis of the detection results.