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The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems. (arXiv:2203.04686v1 [cs.CR])
March 10, 2022, 2:20 a.m. | Giovanni Apruzzese, Luca Pajola, Mauro Conti
cs.CR updates on arXiv.org arxiv.org
Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine
Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations
requiring data where benign and malicious samples are clearly labelled. Such
labels demand costly expert knowledge, resulting in a lack of real deployments,
as well as on papers always relying on the same outdated data. The situation
improved recently, as some efforts disclosed their labelled datasets. However,
most past works used such datasets just as a 'yet another' testbed, overlooking …
detection intrusion detection machine machine learning network systems
More from arxiv.org / cs.CR updates on arXiv.org
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