Adaptive Intrusion Detection in WSNs Using Federated Deep Learning and Homomorphic Encryption

Abstract

Wireless Sensor Networks (WSNs) play a crucial role in various applications, but their vulnerability to malicious nodes and data breaches hinders their full potential. Traditional security methods often struggle to keep pace with evolving attack patterns and can introduce privacy concerns. This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy. Sensor nodes collaboratively train adaptive deep learning models to identify anomalies in real-time, enabling continuous learning and response to evolving threats. Partial Homomorphic Encryption (PHE) safeguards sensitive data throughout the network, ensuring data confidentiality. The trade-off between security and computational cost associated with PHE is acknowledged. The effectiveness of the proposed system FedShield-PHE will be evaluated through simulations, comparing its performance to existing methods across various metrics including detection accuracy, network overhead, and energy consumption. This research offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.