DocumentCode :
3503018
Title :
Optimized feature selection with k-means clustered triangle SVM for Intrusion Detection
Author :
Ashok, Rahul ; Lakshmi, A. Jaya ; Rani, G. Devi Vasudha ; Kumar, Madarapu Naresh
Author_Institution :
Dept. of Comput. Sci., DVR & Dr. HS MIC Coll. of Technol., Kanchikacherla, India
fYear :
2011
fDate :
14-16 Dec. 2011
Firstpage :
23
Lastpage :
27
Abstract :
With the rapid progress in the network based applications, the threat of attackers and security threats has grown exponentially. Misleading of data shows many financial losses in all kind of network based environments. Day by day new vulnerabilities are detected in networking and computer products that lead to new emerging problems. One of the new prevention techniques for network threats is Intrusion Detection System (IDS). Feature selection is the major challenging issues in IDS in order to reduce the useless and redundant features among the attributes (e.g. attributes in KDD cup´99, an Intrusion Detection Data Set). In this paper, we aim to reduce feature vector space by calculating distance relation between features with Information Measure (IM) by evaluating the relation between feature and class to enhance the feature selection. Here we incorporate the Information Measure (IM) method with k-means Cluster Triangular Area Based Support Vector Machine (CTSVM) and SVM (Support Vector Machine) classifier to detect intrusion attacks. By dealing with both continuous and discrete attributes, our proposed method extracts best features with high Detection Rate (DR) and False Positive Rate (FPR).
Keywords :
pattern classification; pattern clustering; security of data; support vector machines; SVM classifier; attacker threat; continuous attribute; detection rate; discrete attribute; false positive rate; feature selection; feature vector space; information measure; intrusion detection; k-means cluster triangular area based support vector machine; network based application; security threat; support vector machines; threat prevention technique; triangle SVM; vulnerability detection; Data mining; Feature extraction; Intrusion detection; Machine learning algorithms; Support vector machine classification; Training; Detection Rate; False Positive Rate; Information Measure; Intrusion Detection; Support Vector Machine; k-means Clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computing (ICoAC), 2011 Third International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4673-0670-6
Type :
conf
DOI :
10.1109/ICoAC.2011.6165213
Filename :
6165213
Link To Document :
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