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Wireless intrusion detection based on different clustering approaches

Publication Type : Conference Paper

Publisher : A2CWiC'10

Source : Proceedings of the 1st Amrita ACM-W Celebration of Women in Computing in India, A2CWiC'10, Coimbatore (2010)

Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-78649429960&partnerID=40&md5=be72caa175a0a5c251c32b04aa5dedb8

ISBN : 9781450301947

Keywords : Algorithms, Classifiers, Clustering approach, Commonly used, Computer crime, Data mining, Data mining techniques, Decision trees, Detectors, Feature extraction, Intrusion detection, Intrusion Detection Systems, K-means, Network security, Performance comparison, Product research, ranking algorithm, Self organizing maps, Wireless intrusion, Wireless local area networks (WLAN), Wireless security, WLAN networks

Campus : Amritapuri

School : School of Biotechnology

Department : Bioinformatics

Year : 2010

Abstract : pWireless security is becoming an important area of product research and development. Wireless Intrusion detection Systems are commonly used in WLAN network for detecting wireless attacks. Classifiers are commonly used as detectors in these systems. Finding an efficient classifier as well selecting best set of features becomes very important for implementing these intrusion detection systems. In this paper, we are finding optimital set of features from collected WLAN data using a Ranking Algorithm technique. Then with the aid of different data mining techniques such as K-Means, self organizing map and decision tree, these features are analyzed and the performance comparison is carried out. © 2010 ACM./p

Cite this Research Publication : A. M. Nambiar, Asha Vijayan, and Nandakumar, A., “Wireless intrusion detection based on different clustering approaches”, in Proceedings of the 1st Amrita ACM-W Celebration of Women in Computing in India, A2CWiC'10, Coimbatore, 2010.

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