CTRL: Contrastive Traffic Recognition with Lightweight network

Network traffic classification plays a vital part in network security and management. Facing the growing sophistication of encryption techniques, various works focus to acquire underlying features and try to achieve more advanced identification. However, current methods mostly depend on pre-training or using large models to fit implicit relationship, which could cause imbalance unsupervised learning due to long-tail distribution and may be impractical for intrusion detection because of large cost. Therefore, we propose a non-pretrained lightweight framework, termed Contrastive Traffic Recognition with Lightweight network CTRL , to fully explore spatial-temporal features in traffic. Specifically, two-stream architecture is adopted to decouple the mixed feature extraction while lightweight encoder is further improved to avoid weak representation. By employing contrastive loss, single model can grasp common knowledge from different views, which realizes better traffic recognition. Extensive experiments conducted on six public traffic datasets from various tasks validate the more superior performances of our CTRL which maintains the fewest parameters, compared to state-of-the-art approaches with an average improvement of 7.5 .