AppFineGraph: Hierarchical Mobile Encrypted Traffic Classification at Multi-Granularities via a Branch Graph Neural Network

Shengbao Li, Zhuohang Lv, Tianning Zang, Lanqi Yang, Qian Qiang

Abstract: Most of existing methods for classifying mobile encrypted traffic are primarily designed for coarse-grained scenarios, focusing on classification at the app name level. This implies that these methods can effectively categorize traffic into app names, but possibly performing poorly when it comes to finer-grained classification of in-app activities such as posting comments or location navigation. Classifying in-app activities poses a greater challenge compared to app name-level classification, as different activities within the same app often adopt similar API libraries, resulting in closely resembling traffic patterns. A more formidable challenge is achieving generic classification that supports both the granularity of app names and in-app activities. It is complex for a single classifier to effectively integrate feature information at multiple levels. Addressing these issues, we propose a novel encrypted traffic classification approach – AppFineGraph. AppFineGraph employs a hierarchical classification framework based on Branch Neural Network (B-NN), enabling the capture of both global and local information at different levels. This approach facilitates multi-granular traffic classification for both app names and in-app activities. Additionally, AppFineGraph introduces a graph neural network for feature representation, effectively mining flow features and correlations from encrypted traffic. In comparison to state-of-the-art approaches, experiments demonstrate that AppFineGraph achieves superior performance across all granularities.

External IDs:dblp:conf/ijcnn/LiLZYQ24