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- W3121034744 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." @default.
- W3121034744 created "2021-01-18" @default.
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- W3121034744 date "2021-01-01" @default.
- W3121034744 modified "2023-10-13" @default.
- W3121034744 title "Mining Graph-Fourier Transform Time Series for Anomaly Detection of Internet Traffic at Core and Metro Networks" @default.
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- W3121034744 doi "https://doi.org/10.1109/access.2021.3050014" @default.
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