CUED Publications database

Mining Graph-Fourier Transform Time Series for Anomaly Detection of Internet Traffic at Core and Metro Networks

Herrera, M and Proselkov, Y and Perez-Hernandez, M and Parlikad, AK (2021) Mining Graph-Fourier Transform Time Series for Anomaly Detection of Internet Traffic at Core and Metro Networks. IEEE Access, 9. pp. 8997-9011.

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Abstract

This article proposes a framework to analyse traffic-data processes on a long-haul backbone infrastructure network providing internet services at a national level. This type of network requires low latency and fast speed, which means there is a large demand for research focusing on near real-time decision-making and resilience assessment. To this aim, this article proposes two innovative, complementary procedures: a multi-view approach for the topology analysis of a backbone network at a static level and a time-series mining approach of the graph signal for modelling the traffic dynamics. The combined framework provides a deeper understanding of a backbone network than classical models, allowing for backbone network optimisation operations and management at near real-time. This methodology was applied to the backbone infrastructure of a major UK internet service provider. Doing so increased accuracy and computational efficiency for detecting where and when anomalies and pattern irregularities occur in the network signal.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div E > Manufacturing Systems
Depositing User: Cron Job
Date Deposited: 03 Jan 2021 20:17
Last Modified: 13 Apr 2021 10:16
DOI: 10.1109/ACCESS.2021.3050014