Network Traffic Obfuscation against Traffic Classification. (31st August 2022)

• Record Type: Journal Article
• Authors: Liu, Likun; Yu, Haining; Yu, Shilin; Yu, Xiangzhan
• Other Names: Guo Yanhui Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-08-31
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/3104392
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : In recent years, tremendous progress has been made in network traffic classification and its use has become ubiquitous in many emerging applications, such as Internet censorship in many countries and ISP traffic engineering. However, the traffic analysis of intermediaries brings the risk of privacy disclosure to users. This paper presents a network traffic obfuscation technology to resist traffic classification. It deceives the machine learning and deep learning models by generating adversarial samples. The adversarial samples generation algorithm includes a white-box attack algorithm based on fuzzy strategy and a black-box attack algorithm based on smote data enhancement. Experiments based on the ISCXTor2016 public data set show that the MIM algorithm has the best performance in white-box attacks, and the obfuscation success rate of DNN and LSTM models is 90%. In the black-box attack, the obfuscation effect of LSTM is the best, while CNN has stronger robustness.