NDLI: Anomaly detection using baseline and K-means clustering

Content Provider	IEEE Xplore Digital Library
Author	Lima, M.F. Zarpelão, B.B. Sampaio, L.D.H. Rodrigues, J.J.P.C. Abrão, T. Proença, M.L.
Copyright Year	2010
Description	Author affiliation: Instituto de Telecomunicaç ões, University of Beira Interior, Covilhã, Portugal (Rodrigues, J.J.P.C.) \|\| School of Elect. & Comp. Engineering, University of Campinas (UNICAMP), Brazil (Zarpelão, B.B.) \|\| Computing Science Department, State University of Londrina (UEL), Brazil (Lima, M.F.; Sampaio, L.D.H.; Abrão, T.; Proença, M.L.)
Abstract	Anomaly detection refers to methods that provide warnings of unusual behaviors which may compromise the security and performance of communication networks. In this paper it is proposed a novel model for network anomaly detection combining baseline, K-means clustering and particle swarm optimization (PSO). The baseline consists of network traffic normal behavior profiles, generated by the application of Baseline for Automatic Backbone Management (BLGBA) model in SNMP historical network data set, while K-means is a supervised learning clustering algorithm used to recognize patterns or features in data sets. In order to escape from local optima problem, the K-means is associated to PSO, which is a meta-heuristic whose main characteristics include low computational complexity and small number of input parameters dependence. The proposed anomaly detection approach classifies data clusters from baseline and real traffic using the K-means combined with PSO. Anomalous behaviors can be identified by comparing the distance between real traffic and cluster centroids. Tests were performed in the network of State University of Londrina and the obtained detection and false alarm rates are promising.
Starting Page	305
Ending Page	309
File Size	196730
Page Count	5
File Format	PDF
ISBN	9781424486632
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-23
Publisher Place	Croatia
Access Restriction	Subscribed
Rights Holder	University of Split, FESB
Subject Keyword	Clustering algorithms Alarm systems Data mining Particle swarm optimization Monitoring Unsupervised learning
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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