Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks

TL;DR

This paper introduces a novel randomization-based method to enhance the robustness of machine learning models, especially random forests, against evasion attacks in security applications like intrusion detection and spam filtering.

Contribution

It proposes a new approach that incorporates randomization during training and application, significantly improving model robustness against adversarial evasion tactics.

Findings

Randomization improves robustness of models against evasion attacks.

The approach enhances the security of random forest classifiers.

Experiments demonstrate increased resilience in intrusion detection and spam filtering.

Abstract

Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection models use and modify or remove those features to avoid detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called {\em randomization} is underexplored. This paper proposes a novel randomization-based approach to improve robustness of machine learning models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has some degree of randomness. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method. We also discuss how this approach can be applied to other ML models.