AdvNet: Revealing Performance Issues in Network Protocols by Generating Adversarial Environments
Shehab Sarar Ahmed, William Sentosa, Yinjie Zhang, Yoav Lebendiker, Michael Shnaiderman, Tomer Gilad, Nathan H. Jay, Brighten Godfrey, Michael Schapira

TL;DR
AdvNet is an automated system that generates adversarial network environments to test and reveal performance issues and hidden bugs in network protocol implementations, especially congestion control protocols.
Contribution
It introduces a machine learning-based approach to automatically create challenging environments, uncovering unseen protocol limitations and bugs in Linux kernel congestion control implementations.
Findings
AdvNet successfully generated adversarial scenarios for 27 kernel-space CC implementations.
The system uncovered previously unnoticed Linux kernel bugs and limitations.
Results demonstrate the value of automated adversarial testing for protocol robustness.
Abstract
Infrastructure protocols like Congestion Control (CC) seek to provide reliable performance across a wide range of Internet environments. Currently, protocol designers assess performance through hand-designed test cases or data sets captured from real environments. However, such approaches may inadvertently overlook critical facets of the algorithm's behavior when they encounter an unanticipated environment or workload. We seek to understand the unanticipated with AdvNet, a system that automatically generates adversarial network environments that cause a target protocol implementation to perform poorly. AdvNet employs machine learning-based optimization to generate environments, and incorporates a robust noise-handling mechanism to mitigate the variability inherent in real-world protocol performance. Although our approach is more general, this paper focuses specifically on transport…
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