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Network Monitoring Method Based on Self-learning and Multi-dimensional Analysis

Authors:
Isao Shimokawa
Toshiaki Tarui

Keywords: Monitor; Network fault; Mahalanobis distance

Abstract:
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 achieves multi-dimensional analysis by using Mahalanobis distance. A prototype monitoring system was constructed and evaluated. The evaluation results indicate that the monitoring system can accurately detect a hidden change in network-traffic condition and reduce the number of unnecessary alarms for monitoring excess bandwidth according to a set threshold.

Pages: 47 to 53

Copyright: Copyright (c) IARIA, 2012

Publication date: October 21, 2012

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-227-1

Location: Venice, Italy

Dates: from October 21, 2012 to October 26, 2012