Robustness of ML-Enhanced IDS to Stealthy Adversaries

TL;DR

This paper evaluates the robustness of ML-enhanced intrusion detection systems, specifically autoencoder-based anomaly detectors, against stealthy adversarial poisoning in training data, showing they maintain effectiveness despite data contamination.

Contribution

It introduces an analysis of the robustness of autoencoder-based IDS to poisoned training data, a critical aspect for real-world deployment of ML-based security systems.

Findings

Autoencoder-based IDS remain effective despite malicious data poisoning.

Training with mixed benign and malicious data does not significantly degrade detection performance.

The study highlights the resilience of ML-enhanced IDS against stealthy adversarial attacks.

Abstract

Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have demonstrated the capacity to efficiently build a prototype of "normal" cyber behaviors in order to detect cyber threats' activity with greater accuracy than traditional rule-based IDS. Because these are largely black boxes, their acceptance requires proof of robustness to stealthy adversaries. Since it is impossible to build a baseline from activity completely clean of that of malicious cyber actors (outside of controlled experiments), the training data for deployed models will be poisoned with examples of activity that analysts would want to be alerted about. We train an autoencoder-based anomaly detection system on network activity with various proportions of malicious activity mixed in and demonstrate that they are robust to this sort of poisoning.