NDLI: Anomaly detection using Support Vector Machine classification with k-Medoids clustering

Content Provider	IEEE Xplore Digital Library
Author	Chitrakar, R. Huang Chuanhe
Copyright Year	2012
Description	Author affiliation: School of Computer, Wuhan University, Wuhan, Hubei, China (Chitrakar, R.; Huang Chuanhe)
Abstract	Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods — specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.
Starting Page	1
Ending Page	5
File Size	312970
Page Count	5
File Format	PDF
ISBN	9781467325912
ISSN	2157068X
e-ISBN	9781467325905
DOI	10.1109/AHICI.2012.6408446
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-23
Publisher Place	Nepal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Anomaly Detection Accuracy Intrusion detection Clustering algorithms Classification algorithms Support Vector Machine Data mining Niobium K-medoids Clustering Naïve Bayes Classification
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
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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