Poisoning Network Flow Classifiers
Giorgio Severi, Simona Boboila, Alina Oprea, John Holodnak, Kendra, Kratkiewicz, Jason Matterer

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…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Anomaly Detection Techniques and Applications · Adversarial Robustness in Machine Learning
