Mobile networks are evolving toward service-aware operation to support heterogeneous applications with diverse quality-ofservice (QoS) and quality-of-experience (QoE) requirements. In practice, however, the widespread use of encryption makes service types difficult to observe directly, which limits the deployment of service-aware optimization in operational networks. Recent raw-byte representation learning methods have improved offline encrypted-traffic classification, but their robustness in realistic deployment remains limited due to temporal drift and the lack of explicit labels for online adaptation. This paper proposes Stable Encrypted-Traffic Representation and Online Adaptation (SEROA), a deployment-oriented framework for encryptedtraffic classification in service-aware mobile networks. In the offline stage, SEROA exploits protocol structure to learn temporally stable representations by applying masked modeling to stable fields, protocolaware transformations and contrastive learning to unstable fields, and time-adversarial learning to suppress time-specific cues. In the online stage, SEROA uses feedback records containing QoE measurements, wireless states, and allocation decisions to update the deployed classifier through contextual-bandit learning. Experiments show that SEROA consistently outperforms representative pretrained baselines under both random and time-based evaluation protocols, and remains effective under QoE-driven online adaptation. These results demonstrate the value of stable representation learning and online adaptation for practical service-aware mobile networks.
SSL has been adopted for encrypted traffic classification to learn representations from unlabeled traces and reduce reliance on manual feature engineering. However, recent studies show that data leakage can inflate reported performance and that pretrained models can underperform shallow classifiers once leakage is controlled. These findings raise questions about what information SSL can learn from encrypted traffic and how different pretraining mechanisms exploit this information. In this paper, we systematically investigate these questions by examining the sources of learnable information, the effectiveness of contextual pretraining, alternative self-supervision mechanisms, and the limits of representation learning. We first find that visible protocol fields retain contextual dependencies that are strongest between adjacent bytes, while payload interventions establish the classification value of retained plaintext structure. Guided by these insights, we investigate byte-level contextual learning through ByteBERT and TrafficDCNet, showing that aligning prediction granularity with local dependencies produces more discriminative representations. We further examine cross-view contrastive learning through AlignTraffic and find that complementary supervision improves representation quality beyond contextual prediction alone. Across four public classification tasks, AlignTraffic outperforms shallow learning by 5.97% to 30.70%, with additional evaluations on industrial traffic demonstrating its effectiveness in broader settings. Finally, evaluations on Tor traffic and individual packets reveal limitations when application information is concealed or unevenly distributed. These findings provide a systematic understanding of how observable information and self-supervision mechanisms shape the effectiveness and scope of representation learning for encrypted traffic.