Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems
In: ISSN: 1424-8220 ; Sensors ; https://hal.science/hal-04224790 ; Sensors, 2023, 2023
Online
academicJournal
Zugriff:
International audience ; 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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Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems
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Autor/in / Beteiligte Person: | Hajj, Suzan ; Azar, Joseph ; Bou Abdo, Jacques ; Demerjian, Jacques ; Guyeux, Christophe ; Makhoul, Abdallah ; Ginhac, Dominique ; Imagerie et Vision Artificielle Dijon (ImViA) ; Université de Bourgogne (UB) ; Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST) ; Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC) ; Université Bourgogne Franche-Comté COMUE (UBFC)-Université Bourgogne Franche-Comté COMUE (UBFC) ; University of Cincinnati (UC) ; Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB) ; Université de Technologie de Belfort-Montbeliard (UTBM)-Université de Bourgogne (UB)-Université Bourgogne Franche-Comté COMUE (UBFC)-Centre National de la Recherche Scientifique (CNRS) |
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Zeitschrift: | ISSN: 1424-8220 ; Sensors ; https://hal.science/hal-04224790 ; Sensors, 2023, 2023 |
Veröffentlichung: | HAL CCSD ; MDPI, 2023 |
Medientyp: | academicJournal |
DOI: | 10.3390/s23167038 |
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