TCGCL:基于图对比学习的复杂网络流量分类算法

Network traffic classification technology plays a crucial role in the field of network security. Modern network architecture is highly complex, and various abnormal situations will inevitably be encountered during network traffic transmission. To this end, this paper proposes a stability index to evaluate the algorithm's resistance to data anomaly interference. In addition, a traffic classification algorithm TCGCL (traffic classification graph contrastive learning) is proposed based on graph contrastive learning. It can simultaneously extract the payload characteristics within network traffic and the connectivity relationship characteristics between network traffic, more comprehensively preserving the effective information of data. Based on this, through data augmentation technology, it simulates the abnormal state of network traffic, greatly improving the classification performance of the algorithm in the case of data anomalies. In addition, based on protocol analysis techniques, this paper studies the construction of graph structured data in the process of traffic classification and proposes a high-quality and low dimensional attribute generation method. The experiment shows that compared with the baseline algorithm, TCGCL reduces the sample input dimension by about 80% with almost the same accuracy. For complex network communication environments, TCGCL conducts noise obfuscation on test samples and simulates abnormal traffic situations. The results show that TCGCL can still maintain high classification accuracy even under abnormal traffic conditions, and its stability index is significantly ahead of the baseline algorithm.