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Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction
March 29, 2024, 4:10 a.m. | Ruoyu Li, Qing Li, Yu Zhang, Dan Zhao, Xi Xiao, Yong Jiang
cs.CR updates on arXiv.org arxiv.org
Abstract: Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput, lack of interpretability, and high maintenance costs. Recent in-network intelligence (INI) exploits programmable switches to offer line-rate deployment of NIDS. Nevertheless, current in-network NIDS are either model-specific or only apply to supervised models. In this paper, we propose Genos, a general in-network framework for unsupervised A-NIDS by rule extraction, which consists of a Model …
arxiv attacks cs.cr current deployment detect detection exploits extraction general high intelligence intrusion intrusion detection line low maintenance network network intrusion nids offer rate solutions switches systems
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