Multi-Scale Contrastive Attention Representation Learning for Encrypted Traffic Classification
Pages 4173 - 4177
Abstract
Encrypted traffic classification is essential for network security and management. However, the encrypted nature makes it challenging to extract representative features from raw traffic data. Existing end-to-end methods ignore byte correlations within packets and potential correlations among packets, hindering the learning of real traffic semantics and leading to suboptimal performance. This paper proposes MsETC, a multi-scale contrastive attention representation learning method for encrypted traffic classification. MsETC divides the raw packet byte sequence into multi-scale patches and then extracts dual views for contrastive learning from both the inter-patch and intra-patch perspectives. This allows the model to capture correlations among bytes within a packet as well as the potential interactions between packets. Extensive experiments on real-world datasets demonstrate that the proposed method achieves superior classification performance with lower complexity.
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Index Terms
- Multi-Scale Contrastive Attention Representation Learning for Encrypted Traffic Classification
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Published: 21 October 2024
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- Hong Kong UGC General Research Fund
- HKU-SCF FinTech Academy Project Grant
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CIKM '24
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CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2024
ID, Boise, USA
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