An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection

arXiv:2402.16912v1 Announce Type: cross
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, …

abstract adversarial arxiv attacks become benchmark computer cs.cr cs.lg cyber detection enterprise enterprises machine machine learning ml models network networks robustness type