AdvNet: Revealing Performance Issues in Network Protocols by Generating Adversarial Environments
2026-05-01 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors created a system called AdvNet that automatically finds tricky network situations where internet protocols called congestion control (CC) don't work well. Instead of relying on normal tests or real-world data that might miss problems, AdvNet uses machine learning to create hard network conditions to test these protocols. They tested 27 different CC versions in the Linux kernel and found new bugs and weaknesses that weren’t known before. This suggests that making unusual test cases can help improve how these protocols perform in unexpected environments.
Congestion ControlTransport ProtocolsMachine Learning OptimizationAdversarial TestingLinux KernelNetwork PerformanceMulti-path Congestion ControlRobustnessInternet Protocols
Authors
Shehab Sarar Ahmed, William Sentosa, Yinjie Zhang, Yoav Lebendiker, Michael Shnaiderman, Tomer Gilad, Nathan H. Jay, Brighten Godfrey, Michael Schapira
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 \sys, 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 protocols and their CC implementations. We showcase AdvNet's capability to create adversarial scenarios for 27 kernel-space implementations of both single-path and multi-path CC protocols, for several use cases with different performance goals. AdvNet identifies problematic network conditions that expose previously unnoticed Linux kernel bugs and uncovers hidden limitations in CC implementations, and provides insights about robustness. These results suggest that automated adversarial testing can be a valuable tool in protocol development, and that robustness is a useful new dimension for benchmarking CC protocols.