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

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Publicado en:Symmetry vol. 17, no. 9 (2025), p. 1580-1597
Autor principal: Liu, Ying
Otros Autores: Liu, Xing, Yu, Hao, Bowen, Guo, Liu, Xiao
Publicado:
MDPI AG
Materias:
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
Acceso en línea:Citation/Abstract
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Resumen: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
Fuente:Engineering Database