Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2025
Abstract
Encrypted traffic classification occupies a significant role in cybersecurity and network management. The existing encrypted traffic classification technology mostly relies on intra-flow semantics for extracting features. However, considering that some attack behaviors inherently have similar patterns to legitimate behaviors, and powerful adversaries could simulate benign users to conceal their attack intentions, intra-flow features may be similar between different categories. In this paper, we propose TrafficScope, a time-wavelet fusion network based on Transformer to enhance the performance of encrypted traffic classification. Specifically, in addition to using intra-flow semantics, TrafficScope also extracts contextual information to construct more comprehensive representations. Moreover, to cope with the non-stationary and dynamic contextual traffic, we employ wavelet transform to extract invariant features. For feature fusion, the cross-attention mechanism is adopted to inline combine temporal and wavelet-domain features. We extensively evaluate TrafficScope compared with 7 state-of-the-art baselines based on four groups of real-world traffic datasets, the results show that TrafficScope outperforms existing methods. We conduct a series of experiments in terms of similar intra-flow feature evaluation, data pollution, flow manipulations, and dynamic context to demonstrate the robustness and stability of the proposed method. Furthermore, we produce additional experiments to present the potential of TrafficScope in cross-dataset scenarios.
Keywords
Traffic Classification, Wavelet Analysis, Attention Mechanism
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
KDD '25: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, Toronto, Canada, August 3-7
First Page
2089
Last Page
2100
ISBN
9798400712456
Identifier
10.1145/3690624.3709315
Publisher
ACM
City or Country
New York
Citation
ZHAO, Ziming; SONG, Zhuoxue; XIE, Xiaofei; LI, Zhaoxuan; YU, Jiongchi; ZHANG, Fan Terry; and LI, Tingting.
Towards context-aware traffic classification via time-wavelet fusion network. (2025). KDD '25: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, Toronto, Canada, August 3-7. 2089-2100.
Available at: https://ink.library.smu.edu.sg/sis_research/10304
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.