PRIVACY-PRESERVING INTRUSION DETECTION IN IOT USING FEDERATED LEARNING: A REVIEW
Keywords:
Federated Learning, Intrusion Detection, Internet Of Things, Privacy Preservation, Differential Privacy, Secure Aggregation, Network SecurityAbstract
The Internet of Things (IoT) is rapidly growing and consequently adding to the attack surface of connected infrastructures, making intrusion detection an important requirement. Traditional IDSs are designed to gather network traffic in a central location and involve a lot of privacy issues as well as clash with data protection laws. Federated learning (FL) has been proposed as a viable paradigm to train a global detection model across local IoT devices while retaining the raw data at the local server. In this review, the recent developments of privacy-preserving intrusion detection in IoT based on federated learning are presented. We outline the Federated training pipeline, aggregation of local updates, and mechanisms to make it resist inference and poisoning attacks, namely differential privacy, secure aggregation and homomorphic encryption. We also discuss representative studies, provide summary of some widely used datasets and an analysis of accuracy–privacy trade-off. Finally we state some open problems concerning heterogeneity, communication cost and robustness and we point out interesting research lines.
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