AdvNet generates adversarial network environments via ML optimization to expose performance issues, bugs, and limitations in 27 congestion control protocol implementations.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.NI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
diffRL enables verification of symbolic properties over input ranges for DRL agents in adaptive video streaming, wireless resource management, and congestion control by decomposing them into tractable sub-properties for existing DNN verifiers.
citing papers explorer
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AdvNet: Revealing Performance Issues in Network Protocols by Generating Adversarial Environments
AdvNet generates adversarial network environments via ML optimization to expose performance issues, bugs, and limitations in 27 congestion control protocol implementations.
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Analyzing Symbolic Properties for DRL Agents in Systems and Networking
diffRL enables verification of symbolic properties over input ranges for DRL agents in adaptive video streaming, wireless resource management, and congestion control by decomposing them into tractable sub-properties for existing DNN verifiers.