In social networks, encrypted communication has become increasingly prevalent to protect user privacy. However, this also provides attackers with opportunities to launch covert attacks via encrypted traffic. Traditional traffic detection methods relying on rules and payload analysis fail due to content encryption. As a...
Xiao-Wei Zhao, Ming-Shu He, Xiao-Juan Wang et al.· IEEE Transactions on Computa...· 0 citations
Network traffic classification is central to security monitoring and network management in heterogeneous environments. Existing deep learning approaches are often trained for a single scenario and require large amounts of labeled data, making them difficult to reuse when applications, traffic types, or encryption setti...
This study introduces an adversarial training optimization framework that incorporates hierarchical label encoding and prompt learning, designed to enhance model robustness and generalization in threat detection.
Yi-Qing Luo, Ming-Shu He, Xiao-Juan Wang· Proceedings of the Thirty-Fi...· 0 citations
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