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Regular Paper

A New Approach to Multivariate Network Traffic Analysis

Department of Computer Science, Texas A&M University, Commerce 75428, U.S.A.
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, U.S.A.

A preliminary version of the paper was published in the Proceedings of ICCCN 2017.

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Abstract

Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers.

Keywords

network traffic analysismultivariate analysistime-series similaritynetwork monitoring

Electronic Supplementary Material

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Journal of Computer Science and Technology
Volume 34 Issue 2,
March 2019
Pages 388-402
DOI: 10.1007/s11390-019-1915-y
Cite this article:
Kim J, Sim A. A New Approach to Multivariate Network Traffic Analysis. Journal of Computer Science and Technology, 2019, 34(2): 388-402. https://doi.org/10.1007/s11390-019-1915-y
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