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An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
Feb. 28, 2024, 5:11 a.m. | Jo\~ao Vitorino, Miguel Silva, Eva Maia, Isabel Pra\c{c}a
cs.CR updates on arXiv.org arxiv.org
Abstract: As cyber-attacks become more sophisticated, improving the robustness of Machine Learning (ML) models must be a priority for enterprises of all sizes. To reliably compare the robustness of different ML models for cyber-attack detection in enterprise computer networks, they must be evaluated in standardized conditions. This work presents a methodical adversarial robustness benchmark of multiple decision tree ensembles with constrained adversarial examples generated from standard datasets. The robustness of regularly and adversarially trained RF, XGB, …
adversarial arxiv attack attacks benchmark computer conditions cs.cr cs.lg cyber cyber-attack detection enterprise enterprises intrusion intrusion detection machine machine learning ml models network networks robustness
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