Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems

TL;DR

This paper presents an explainable, privacy-preserving intrusion detection framework for IoMT systems using federated learning and neural networks, effectively detecting attacks while maintaining data confidentiality and interpretability.

Contribution

It introduces a novel framework combining federated learning, neural networks, and explainable AI for secure, private, and interpretable intrusion detection in IoMT environments.

Findings

Federated learning achieves comparable performance to centralized methods.

The framework enhances privacy and interpretability of intrusion detection.

High detection accuracy across multiple attack types.

Abstract

The Internet of Medical Things transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention. This innovative method revolutionizes healthcare by facilitating early disease detection and tailored care, particularly in chronic disease management, where IoMT automates treatments based on real-time health data collection. Nonetheless, its benefits are countered by significant security challenges that endanger the lives of its users due to the sensitivity and value of the processed data, thereby attracting malicious interests. Moreover, the utilization of wireless communication for data transmission exposes medical data to interception and tampering by cybercriminals. Additionally, anomalies may arise due to human error, network interference, or hardware malfunctions. In this context, anomaly detection based on Machine Learning (ML) is an interesting solution, but it comes up against obstacles in terms of explicability and privacy protection. To address these challenges, a new framework for Intrusion Detection Systems is introduced, leveraging Artificial Neural Networks for intrusion detection while utilizing Federated Learning for privacy preservation. Additionally, eXplainable Artificial Intelligence methods are incorporated to enhance model explanation and interpretation. The efficacy of the proposed framework is evaluated and compared with centralized approaches using multiple datasets containing network and medical data, simulating various attack types impacting the confidentiality, integrity, and availability of medical and physiological data. The results offer compelling evidence that the FL method performs comparably to the centralized method, demonstrating high performance. Additionally, it affords the dual advantage of safeguarding privacy and providing model explanation while adhering to ethical principles.