Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory

• Published in: IEEE transactions on big data Vol. 8; no. 1; pp. 241 - 252
• Main Authors: Yao, Haipeng, Liu, Chong, Zhang, Peiying, Wu, Sheng, Jiang, Chunxiao, Yu, Shui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: attention mechanism; Big Data; Classification; Communications traffic; Datasets; Deep learning; encrypted traffic; Encryption; Feature extraction; Intelligent networks; LSTM; Network traffic classification; Protocols; Quality of service architectures; Recurrent neural networks; Short term; Virtual private networks
• ISSN: 2332-7790; 2372-2096
• DOI: 10.1109/TBDATA.2019.2940675

Network traffic classification has become an important part of network management, which is beneficial for achieving intelligent network operation and maintenance, enhancing the network quality of service (QoS), and for network security. Given the rapid development of various applications and protocols, more and more encrypted traffic has emerged in networks. Traditional traffic classification methods exhibited the unsatisfied performance since the encrypted traffic is no longer in plain text. In this work, we modeled the time-series network traffic by the recurrent neural network (RNN). Moreover, the attention mechanism was introduced for assisting network traffic classification in the form of the following two models, the attention aided long short term memory (LSTM) as well as the hierarchical attention network (HAN). Finally, relying on the ISCX VPN-NonVPN dataset, extensive experiments were conducted, showing that the proposed methods achieved 91.2 percent in accuracy while the highest accuracy of other methods was 89.8 percent relying on the same dataset.