A Review of Privacy-Preserving Intrusion Detection for Healthcare Edge-IOT

The rapid adoption of Edge Computing and Internet of Things (IoT) technologies in healthcare has enabled real-time patient monitoring and low-latency clinical decision support. However, the distributed and resource-constrained nature of Edge-IoT systems makes them highly vulnerable to cyber-attacks such as data breaches, ransomware, and denial-of-service, which threaten patient privacy and system reliability. Traditional centralized AI-based intrusion detection systems (IDS) face limitations in privacy preservation, scalability, and suitability for edge environments. To address these challenges, this paper proposes a secure and privacy-preserving cyber-attack detection framework that integrates an optimized LSTM Gated Multi-Layer Perceptron Neural Network (LSTMG-MLPNN) with Federated Learning (FL) and Med-Chain block chain technology. Federated Learning enables collaborative model training without sharing raw patient data, and Med-Chain with lattice encryption ensures secure aggregation, trust management, and auditability.The proposed system provides an effective, scalable, and privacy-aware solution for cyber-attack detection in healthcare Edge-IoT environments.

Edge Computing, Federated learning, Long Short Term Memory, Multilayer Perceptron Neural Network.

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