NetTTT: Online Self-Adaptation Inference via Test-Time Training for Encrypted Traffic Classification

Encrypted traffic classification (ETC) serves as a pivotal research for network measurement and Quality of Service (Qos) management. However, most current approaches rely on statically frozen parameters after training, which remain vulnerable to performance degradation under real-world distribution shifts caused by dynamic network conditions, protocol evolution, and adversarial obfuscation techniques. To tackle this issue, we propose NetTTT, an online self-adaptation framework that, for the first time, performs continuous representation refinement directly at inference time without retraining. NetTTT leverages a hierarchical feature encoder that constructs structured dual-modal representations from raw traffic sessions, coupled with a pre-trained generic representation extractor to powerfully capture transferable contextual features, and incorporates a lightweight test-time adapter, which performs unsupervised, layer-wise parameter optimization during inference to dynamically align traffic representations with test-time traffic distributions, followed by a compact classifier that efficiently maps adapted representations to traffic categories. Experimental results reveal the superiority of NetTTT across diverse ETC tasks and few-shot scenarios with accuracy improvements ranging from 0.08% to 9.34% over state-of-the-art baselines. By enabling dynamic adaptation without labeled data or full model retraining, NetTTT bridges the gap between static pre-trained models and evolving encrypted traffic, highlighting its potential to provide a practical solution for real-world scenarios.