Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Switzerland), 2023-08, Vol.23 (16), p.7038
Main Authors:	Hajj, Suzan, Azar, Joseph, Bou Abdo, Jacques, Demerjian, Jacques, Guyeux, Christophe, Makhoul, Abdallah, Ginhac, Dominique
Format:	Article
Language:	eng
Subjects:	Algorithms Clustering Collaboration Computer Science Cryptography and Security Detectors Distributed, Parallel, and Cluster Computing Efficiency Emerging Technologies Energy consumption federated learning Internet of Things lightweight intrusion detection lightweight sampling Modeling and Simulation Multiagent Systems Privacy Safety and security measures Sampling techniques Security management Security systems semi-supervised learning Software Engineering Ubiquitous Computing
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Summary:	With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system’s performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.
ISSN:	1424-8220 1424-8220