Bottom Aggregating, Top Separating: An Aggregator and Separator Network for Encrypted Traffic Understanding

Encrypted traffic classification refers to the task of identifying the application, service or malware associated with network traffic that is encrypted. Previous methods mainly have two weaknesses. Firstly, from the perspective of word-level (namely, byte-level) semantics, current methods use pre-training language models like BERT, learned general natural language knowledge, to directly process byte-based traffic data. However, understanding traffic data is different from understanding words in natural language, using BERT directly on traffic data could disrupt internal word sense information so as to affect the performance of classification. Secondly, from the perspective of packet-level semantics, current methods mostly implicitly classify traffic using abstractive semantic features learned at the top layer, without further explicitly separating the features into different space of categories, leading to poor feature discriminability. In this paper, we propose a simple but effective Aggregator and Separator Network (ASNet) for encrypted traffic understanding, which consists of two core modules. Specifically, a parameter-free word sense aggregator enables BERT to rapidly adapt to understanding traffic data and keeping the complete word sense without introducing additional model parameters. And a category-constrained semantics separator with task-aware prompts (as the stimulus) is introduced to explicitly conduct feature learning independently in semantic spaces of different categories. Experiments on five datasets across seven tasks demonstrate that our proposed model achieves the current state-of-the-art results without pre-training in both the public benchmark and real-world collected traffic dataset. Statistical analyses and visualization experiments also validate the interpretability of the core modules. Furthermore, what is important is that ASNet does not need pre-training, which dramatically reduces the cost of computing power and time. The model code and dataset will be released in https://github.com/pengwei-iie/ASNET.