Poisoning Network Flow Classifiers
Name
3627106.3627123.pdf
Size
2.73 MB
Format
Adobe PDF
Checksum (MD5)
e3c508eefb61ee86328ef45f04a2bfe0
Author(s)
Severi, Giorgio
Boboila, Simona
Oprea, Alina
Holodnak, John
Kratkiewicz, Kendra
Matterer, Jason
Date Issued
December 4, 2023
Publisher
ACM|Annual Computer Security Applications Conference
Citation
Severi, Giorgio, Boboila, Simona, Oprea, Alina, Holodnak, John, Kratkiewicz, Kendra et al. 2023. "Poisoning Network Flow Classifiers."
Version
Final published version
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.
MIT Department
Lincoln Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3627106.3627123
