Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

doi:10.3390/s23167038

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	Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems
	S. Hajj; J. Azar; J. Bou Abdo; J. Demerjian; C. Guyeux; A. Makhoul and D. Ginhac
	2023
发表期刊	Sensors
ISSN	14248220
卷号	23 期号:16
摘要	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. © 2023 by the authors.
DOI	10.3390/s23167038
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收录类别	ei
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文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67501
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	S. Hajj,J. Azar,J. Bou Abdo,et al. Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems[J]. Sensors,2023,23(16).
APA	S. Hajj,J. Azar,J. Bou Abdo,J. Demerjian,C. Guyeux,&A. Makhoul and D. Ginhac.(2023).Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems.Sensors,23(16).
MLA	S. Hajj,et al."Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems".Sensors 23.16(2023).

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