An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection

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, LGBM, and EBM models was evaluated on the original CICIDS2017 dataset, a corrected version of it designated as NewCICIDS, and the HIKARI dataset, which contains more recent network traffic. NewCICIDS led to models with a better performance, especially XGB and EBM, but RF and LGBM were less robust against the more recent cyber-attacks of HIKARI. Overall, the robustness of the models to adversarial cyber-attack examples was improved without their generalization to regular traffic being affected, enabling a reliable detection of suspicious activity without costly increases of false alarms.

arXiv comment15 pages, 8 tables, 2 figures, FPS 2023 conference

References in corpus (6)

• XGBoost: A Scalable Tree Boosting System
• Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
• A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection
• Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification
• Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection
• SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection

Cited by in corpus (1)

• Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection