A Federated Intrusion Detection System for Edge Environments Using Multi-Index Hashing and Attention-Based KNN

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書誌詳細
出版年:	Symmetry vol. 17, no. 9 (2025), p. 1580-1597
第一著者:	Liu, Ying
その他の著者:	Liu, Xing, Yu, Hao, Bowen, Guo, Liu, Xiao
出版事項:	MDPI AG
主題:	Machine learning Accuracy Embedded systems Collaboration Deep learning Neural networks Infrastructure Edge computing Intrusion detection systems Communication Binary codes Communications traffic Sensors Retrieval Codes Privacy Real time Federated learning Distributed processing
オンライン･アクセス:	Citation/Abstract Full Text + Graphics Full Text - PDF
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その他の書誌記述
抄録:	Edge computing offers low-latency and distributed processing for IoT applications but poses new security challenges, due to limited resources and decentralized data. Intrusion detection systems (IDSs) are essential for real-time threat monitoring, yet traditional IDS frameworks often struggle in edge environments, failing to meet efficiency requirements. This paper presents an efficient intrusion detection framework that integrates spatiotemporal hashing, federated learning, and fast K-nearest neighbor (KNN) retrieval. A hashing neural network encodes network traffic into compact binary codes, enabling low-overhead similarity comparison via Hamming distance. To support scalable retrieval, multi-index hashing is applied for sublinear KNN searching. Additionally, we propose an attention-guided federated aggregation strategy that dynamically adjusts client contributions, reducing communication costs. Our experiments on benchmark datasets demonstrate that our method achieves competitive detection accuracy with significantly lower computational, memory, and communication overhead, making it well-suited for edge-based deployment.
ISSN:	2073-8994
DOI:	10.3390/sym17091580
ソース:	Engineering Database