An instruction-tuned LLM with flow-graph alignment classifies encrypted traffic under distribution shift, reporting F1 improvements of up to 18 points over baselines on out-of-distribution benchmarks.
Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non- stationarity,
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Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification
An instruction-tuned LLM with flow-graph alignment classifies encrypted traffic under distribution shift, reporting F1 improvements of up to 18 points over baselines on out-of-distribution benchmarks.