Multi-Granularity Feature Fusion for Enhancing Encrypted Traffic Classification

Encrypted traffic classification, a pivotal process in network security and management, involves analyzing and categorizing data traffic that has been encrypted for privacy and security. This task demands the extraction of distinctive and robust feature representations from content-concealed data to ensure accurate and reliable classification. Traditional approaches have focused on utilizing either the payload of encrypted traffic or statistical features for more precise classification. While these methods achieve relative success, their limitation lies in not harnessing multi-grained features, thus impeding further advance-ments in encrypted traffic classification capabilities. To tackle this challenge, ET-CompBERT is presented, an innovative framework specifically designed for the fusion of multi-granularity features in encrypted traffic, encompassing both payload and global temporal attributes. The extensive experiments reveal that our approach significantly enhances classification performance in data-rich scenarios (achieving up to a +4.43% improvement in certain cases over existing methods) and establishes state-of-the-art results on training sets with different sizes. The source codes will be released after paper acceptance.

@article{Ding2024,
  title     = {Multi-Granularity Feature Fusion for Enhancing Encrypted Traffic Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Quan Ding and Zhengpeng Zha and Yanjun Li and Zhenhua Ling},
  doi       = {10.14569/IJACSA.2024.01504110},
  url       = {https://doi.org/10.14569/IJACSA.2024.01504110}
}