STARNeT: Multidimensional spatial–temporal attention recall network for accurate encrypted traffic classification

Abstract

Network traffic classification is crucial for monitoring network health, detecting malicious activities, and ensuring Quality-of-Service (QoS). The use of dynamic ports and encryption complicates the process, rendering traditional port-based or payload-based classification methods ineffective. Conventional machine learning and statistical approaches often depend on manual feature or pattern extraction by experts, leading to inefficiencies and potential inaccuracies. Deep learning offers a promising alternative, with its inherent capability to autonomously extract patterns and features from data. Nonetheless, the design of existing deep learning models often limits them to high-level semantic feature extraction, neglecting the rich multidimensional spatial and temporal information in network traffic. To address these limitations, this paper introduces STARNet, a deep learning-based model for encrypted traffic classification. STARNet incorporates a dual-stream pathway network architecture that optimizes feature extraction from each pathway. It also features a novel spatiotemporal multidimensional semantic feature recall mechanism, designed to enrich the model’s analytical depth by retaining important information that might be missed when focusing solely on high-level features. Evaluated on two public network traffic datasets, STARNet demonstrates superior accuracy in traffic classification tasks, highlighting its potential to enhance network monitoring and security.