Anomaly Detection Aiming Pro-Active Management of Computer Network Based on Digital Signature of Network Segment

Authors:
Bruno Bogaz Zarpelão;Leonardo De Souza Mendes;Mario Lemes Proença, Jr.
Affiliations:
School of Electrical and Computer Engineering (FEEC), State University of Campinas (UNICAMP), Avenida Albert Einstein, 400, Cidade Universitária, Campinas, Brazil 13083-852;School of Electrical and Computer Engineering (FEEC), State University of Campinas (UNICAMP), Avenida Albert Einstein, 400, Cidade Universitária, Campinas, Brazil 13083-852;Computer Science Department, State University of Londrina (UEL), Londrina, Brazil 86051-990
Venue:
Journal of Network and Systems Management
Year:
2007

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Abstract

Detecting anomalies accurately is fundamental to rapid diagnosis and repair of problems. This paper proposes a novel Anomaly detection system based on the comparison of real traffic and DSNS (Digital Signature of Network Segment), generated by BLGBA (Baseline for Automatic Backbone Management) model, within a hysteresis interval using the residual mean and on the correlation of the detected deviations. Extensive experimental results on real network servers confirmed that our system is able to detect anomalies on the monitored devices, avoiding the high false alarms rate.