Poisoning Network Flow Classifiers

TL;DR

This paper explores the vulnerability of network traffic classifiers to poisoning backdoor attacks, introducing novel trigger crafting and stealthy generation methods to assess attack feasibility and detection challenges.

Contribution

It presents a new trigger crafting strategy using interpretability techniques and develops stealthy trigger generation methods, including Bayesian network models, for network traffic classifiers.

Findings

Poisoning attacks can be effective even at low poisoning rates.

Stealthy trigger generation reduces detectability of poisoning campaigns.

Network classifiers are vulnerable to backdoor poisoning in various scenarios.

Abstract

As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical. This paper focuses on poisoning attacks, specifically backdoor attacks, against network traffic flow classifiers. We investigate the challenging scenario of clean-label poisoning where the adversary's capabilities are constrained to tampering only with the training data - without the ability to arbitrarily modify the training labels or any other component of the training process. We describe a trigger crafting strategy that leverages model interpretability techniques to generate trigger patterns that are effective even at very low poisoning rates. Finally, we design novel strategies to generate stealthy triggers, including an approach based on generative Bayesian network models, with the goal of minimizing the conspicuousness of the trigger, and thus making detection of an ongoing poisoning campaign more challenging. Our findings provide significant insights into the feasibility of poisoning attacks on network traffic classifiers used in multiple scenarios, including detecting malicious communication and application classification.