Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems
文献类型：期刊论文
| 作者 | S. Hajj; J. Azar; J. Bou Abdo; J. Demerjian; C. Guyeux; A. Makhoul and D. Ginhac |
| 刊名 | Sensors
 |
| 出版日期 | 2023 |
| 卷号 | 23期号:16 |
| ISSN号 | 14248220 |
| DOI | 10.3390/s23167038 |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| 源URL | [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). |
入库方式： OAI收割
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