Title: EATIS: an environmentally adaptive traffic identification system for open world networks

Authors: Yulong Liang; Fei Wang; Shuhui Chen; Yunjiao Bo; Na Wang

Addresses: PLA Unit 92493, Huludao, China ' College of Computer, National University of Defense Technology, Changsha, China ' College of Computer, National University of Defense Technology, Changsha, China ' PLA Unit 92493, Huludao, China ' PLA Unit 92493, Huludao, China

Abstract: Traffic identification, as a crucial measure in network management and security, has garnered significant attention from the public for an extended period. Machine learning methods have emerged as promising and effective solutions for identification of encrypted traffic. However, the intricate and ever-changing nature of the network environment often leads to subpar performance of conventional machine learning approaches. In this paper, we conduct a meticulous analysis of the characteristics of network traffic identification tasks, along with a examination of the limitations of previous methods based on experimental evidence. Moreover, we present EACIS, a highly adaptable and comprehensive system that aims to perform traffic identification in open world network scenarios. EACIS incorporates semi-supervised learning and innovative novelty detection techniques for online identification and differentiation of known traffic, unrelated traffic, and zero-day traffic. Experimental assessments performed on NUDT MobileTraffic dataset, which comprises actual traffic data, illustrate the benefits of our proposed approaches.

Keywords: traffic identification; network monitoring; semi-supervised learning; novelty detection; random forest.

DOI: 10.1504/IJICS.2025.149448

International Journal of Information and Computer Security, 2025 Vol.28 No.3, pp.377 - 401

Received: 04 Jun 2024
Accepted: 16 Jan 2025
Published online: 31 Oct 2025 *

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