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Network-monitoring Method based on Self-learning and Multi-dimensional Analysis
unpublished
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A novel network-monitoring system for detecting abnormal network conditions (such as hidden network congestion) is proposed. The proposed monitoring system is based on self-learning and multi-dimensional analysis. It analyzes multiple parameters such as consumed bandwidth, packet size, and arrival interval of network packets simultaneously. By executing high-quality network monitoring it thereby achieves multi-dimensional analysis by use of Mahalanobis distance. A prototype monitoring system
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