Primary Investigator:
Feng Li

Project Members
Garvit Agarwal, Yousef Mohammed Y. Alomayri, Seunghyun Cho, Feng Li​

The rapid digitization of healthcare has led to an explosion of Internet of Medical Things (IoMT) devices, creating a complex and vulnerable attack surface. While Intrusion Detection Systems (IDS) are critical for defense, traditional centralized approaches require the aggregation of sensitive network telemetry, often violating privacy regulations such as HIPAA and GDPR. Federated Learning (FL) offers a privacy-preserving alternative but imposes significant computational and bandwidth overheads unsuitable for resource-constrained medical edge devices. To address these challenges, we propose Temporal Split Learning (TSL), a novel distributed deep learning framework tailored for IoMT security. TSL partitions the neural network execution between the edge (client) and the cloud (server), transmitting only obfuscated intermediate activations ("smashed data") rather than raw data or model weights. We enhance standard Split Learning with Recurrent Neural Networks (RNNs) to capture the temporal sequentiality of Advanced Persistent Threats (APTs) and malware behavior. We evaluate TSL using a high-fidelity, 50-node virtualization testbed generating realistic HL7 and DICOM traffic mixed with live malware detonation. Our results demonstrate that TSL achieves a 98.4\