Graph based encrypted malicious traffic detection with hybrid analysis of multi-view features

At present, the TLS cryptographic protocol is widely deployed. While protecting the security and integrity of transmitted information, it also makes the detection of malicious behavior more difficult. In recent years, researchers have proposed many encrypted malicious traffic detection methods. However, the existing approaches have some shortcomings. Firstly, although researchers have extracted multi-view features from different aspects, which can be divided into vectorized features based on feature engineering and image features based on original data, existing methods cannot fully integrate the features of different forms of expression. Secondly, most of the existing methods do not fully analyze the correlation between different encrypted traffic. Thirdly, the existing methods based on correlation analysis have low processing efficiency and cannot be applied to real networks. In the paper, we present MalDiscovery, a novel technique to discover encrypted malicious traffic to address all the above issues. For encrypted malicious traffic, MalDiscovery constructs an attribute KNN graph, in which encrypted sessions are used as nodes to construct a KNN graph according to the similarity of image features, and vectorized features are used as attributes of corresponding nodes. After that, the GraphSAGE model is used to collect relevant node information through correlation analysis to enrich the embeddings of each node. Finally, we achieve the accurate binary classification of nodes in the graph based on richer embeddings. We use extensive experiments to evaluate the proposed method, and the experiment results show that MalDiscovery can achieve an accuracy of about 99.9%, significantly outperforming all compared methods.