“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.”

Find the paper and the full list of authors at ArXiv.