Mining graph-Fourier transform time series for anomaly detection of internet traffic at core and metro networks

Herrera, Manuel; Proselkov, Yaniv; Perez-Hernandez, Marco; Parlikad, Ajith Kumar

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.

• Journal Article Type: Article
• Acceptance Date: Jan 2, 2021
• Online Publication Date: Jan 8, 2021
• Publication Date: Jan 8, 2021
• Deposit Date: Feb 1, 2022
• Publicly Available Date: Feb 2, 2022
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Pages: 8997-9011
• DOI: https://doi.org/10.1109/ACCESS.2021.3050014
• Public URL: https://uwe-repository.worktribe.com/output/8543507
• Publisher URL: https://ieeexplore.ieee.org/document/9316656