Augmentation and Fusion: Multi-Feature Fusion-Based Self-Supervised Learning Approach for Traffic Tables

As modern networks face increasing demands for superior service and management, Encrypted Traffic Classification (ETC) technology has become increasingly crucial. Considering that traffic data is easy to collect but hard to label, self-supervised ETC methods have attracted more and more attention. Compared to popular methods based on traffic images and text, traffic tables are simple to construct and more suitable for the flow-packet structure. However, existing methods have two problems: (1) The lack of data augmentation methods for tables weakens the performance of self-supervised learning. (2) Most methods only focus on single feature and cannot make full use of distinct features of traffic tables, such as temporal feature. To solve these problems, we propose a multi-feature fusion method based self-supervised learning approach for traffic tables. A new data augmentation method called Random Subsets Selection (RSS) is introduced alongside an effective fusion approach. In this way, temporal features can be successfully extracted and concatenated with the latent representations of input traffic tables. Experimental results from two open datasets and one self-collected dataset have shown that on imbalanced datasets, our method can effectively solve ETC problems even with a small number of labeled data. Empirically, both classification performance and processing speed are improved. Specifically, compared to the state-of-the-art tabular self-supervised learning method, our method achieves the better classification results on all datasets while the processing speed increases by almost two times, from 1.83 tables per second to 3.76 tables per second.