all InfoSec news
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
More from arxiv.org / cs.CR updates on arXiv.org
IDEA: Invariant Defense for Graph Adversarial Robustness
2 days, 1 hour ago |
arxiv.org
FairCMS: Cloud Media Sharing with Fair Copyright Protection
2 days, 1 hour ago |
arxiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Associate Compliance Advisor
@ SAP | Budapest, HU, 1031
DevSecOps Engineer
@ Qube Research & Technologies | London
Software Engineer, Security
@ Render | San Francisco, CA or Remote (USA & Canada)
Associate Consultant
@ Control Risks | Frankfurt, Hessen, Germany
Senior Security Engineer
@ Activision Blizzard | Work from Home - CA