LAUSR

The Artificial Intelligence-enabled Internet of Medical Things (AI-IoMT) envisions the connectivity of medical devices encompassing advanced computing technologies to empower large-scale intelligent healthcare networks. The AI-IoMT continuously monitors patients' health and vital computations via IoMT sensors with enhanced resource utilization for providing progressive medical care services. However, the security concerns of these autonomous systems against potential threats are still underdeveloped. Since these IoMT sensor networks carry a bulk of sensitive data, they are susceptible to unobservable False Data Injection Attacks (FDIA), thus jeopardizing patients' health. This paper presents a novel threat-defense analysis framework that establishes an experience-driven approach based on a deep deterministic policy gradient to inject false measurements into IoMT sensors, computing vitals, causing patients' health instability. Subsequently, a privacy-preserved and optimized federated intelligent FDIA detector is deployed to detect malicious activity. The proposed method is parallelizable and computationally efficient to work collaboratively in a dynamic domain. Compared to existing techniques, the proposed threat-defense framework is able to thoroughly analyze severe systems' security holes and combats the risk with lower computing cost and high detection accuracy along with preserving the patients' data privacy.