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SwiftSession: A Novel Incremental and Adaptive Approach to Rapid Traffic Classification by Leveraging Local Features

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Listed:
  • Tieqi Xi

    (School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Qiuhua Zheng

    (School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China
    Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou 310000, China)

  • Chuanhui Cheng

    (The School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430060, China)

  • Ting Wu

    (Hangzhou Innovation Institute, Beihang University, Hangzhou 310020, China)

  • Guojie Xie

    (Zhejiang Key Laboratory of Open Data, Hangzhou 310000, China)

  • Xuebiao Qian

    (School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Haochen Ye

    (School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Zhenyu Sun

    (School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

Network traffic classification is crucial for effective security management. However, the increasing prevalence of encrypted traffic and the confidentiality of protocol details have made this task more challenging. To address this issue, we propose a progressive, adaptive traffic classification method called SwiftSession, designed to achieve real-time and accurate classification. SwiftSession extracts statistical and sequential features from the first K packets of traffic. Statistical features capture overall characteristics, while sequential features reflect communication patterns. An initial classification is conducted based on the first K packets during the classification process. If the prediction meets the predefined probability threshold, processing stops; otherwise, additional packets are received. This progressive approach dynamically adjusts the required packets, enhancing classification efficiency. Experimental results show that traffic can be effectively classified by using only the initial K packets. Moreover, on most datasets, the classification time is reduced by more than 70%. Unlike existing methods, SwiftSession enhances the classification speed while ensuring classification accuracy.

Suggested Citation

  • Tieqi Xi & Qiuhua Zheng & Chuanhui Cheng & Ting Wu & Guojie Xie & Xuebiao Qian & Haochen Ye & Zhenyu Sun, 2025. "SwiftSession: A Novel Incremental and Adaptive Approach to Rapid Traffic Classification by Leveraging Local Features," Future Internet, MDPI, vol. 17(3), pages 1-20, March.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:3:p:114-:d:1604505
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    References listed on IDEAS

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    1. Lajos Gergely Gyurk'o & Terry Lyons & Mark Kontkowski & Jonathan Field, 2013. "Extracting information from the signature of a financial data stream," Papers 1307.7244, arXiv.org, revised Jul 2014.
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