Robust Network Traffic Classification

By Jun Zhang, Xiao Chen, Yang Xiang, Wanlei Zhou, Jie Wu. IEEE/ACM Transactions on Networking, 2015

359 citations recorded by the source corpus.

As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/c3df12f0546e61c106732cf3f87efe419b1da411

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Citations in the corpus

A Survey of Android Malware Detection with Deep Neural Models (2020, background): “However, the feature engineering process is time-expensive and error-prone, and the produced hand-crafted features are task-specific and often subjective to individual judgment [3, 4].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “Consequentially, joint application of supervised and unsupervised ML for traffic classification [137, 497] has demonstrated success.”
Machine Learning for Networking: Workflow, Advances and Opportunities (2017, methodology): “tion RTC [8]: traffic”
UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities (2018, background): “These classification techniques prioritize the applications when the availability of the bandwidth is limited [44].”
Deep Learning for Encrypted Traffic Classification: An Overview (2018, background): “few recent studies [13] proposed solutions which usually rely on detecting unlabeled clusters and then labeling them.”
A Survey of IoT Applications in Blockchain Systems (2020, background): “Traffic modeling work has been carried out from the Internet [Wu et al. 2018; Zhang et al. 2015, 2012, 2013] to IoT networks [Al-Turjman 2018; Al-Turjman et al. 2017; Masek et al. 2016].”
Detecting and Preventing Cyber Insider Threats: A Survey (2018, background): “Network traffic-based analytics are traditionally developed for intrusion detection [87], [89], [111].”