Article Dans Une Revue Sensors Année : 2023

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Résumé

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.

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hal-04224790 , version 1 (13-12-2023)

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Suzan Hajj, Joseph Azar, Jacques Bou Abdo, Jacques Demerjian, Christophe Guyeux, et al.. Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems. Sensors, 2023, 23 (16), pp.7038 (26). ⟨10.3390/s23167038⟩. ⟨hal-04224790⟩
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