Task-Decomposition based Anomaly Detection of Massive And High-Volatility Session Data on Academic Backbone Network
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説明
<jats:p>The Science Information Network (SINET) is a Japanese academic backbone network for more than 800 universities and research institutions. The characteristic of SINET traffic is that it is enormous and highly variable. In this paper, we present a task-decomposition based anomaly detection of massive and highvolatility session data of SINET. Three main features are discussed: Tash scheduling, Traffic discrimination, and Histogramming. We adopt a task-decomposition based dynamic scheduling method to handle the massive session data stream of SINET. In the experiment, we have analysed SINET traffic from 2/27 to 3/8 and detect some anomalies by LSTM based time-series data processing.</jats:p>
収録刊行物
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- International Journal of Distributed and Parallel systems
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International Journal of Distributed and Parallel systems 12 1-9, 2021-03-31
Academy and Industry Research Collaboration Center (AIRCC)
