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Izvorni znanstveni članak
https://doi.org/10.20532/cit.2016.1002701

Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

Sumaiya Thaseen Ikram ; School of Computing Science and Engineering, VIT University, Chennai, Tamil Nadu, India
Aswani Kumar Cherukuri ; School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India

Puni tekst: engleski, pdf (1 MB) str. 133-148 preuzimanja: 705* citiraj
APA 6th Edition
Ikram, S.T. i Cherukuri, A.K. (2016). Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM. Journal of computing and information technology, 24 (2), 133-148. https://doi.org/10.20532/cit.2016.1002701
MLA 8th Edition
Ikram, Sumaiya Thaseen i Aswani Kumar Cherukuri. "Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM." Journal of computing and information technology, vol. 24, br. 2, 2016, str. 133-148. https://doi.org/10.20532/cit.2016.1002701. Citirano 24.02.2020.
Chicago 17th Edition
Ikram, Sumaiya Thaseen i Aswani Kumar Cherukuri. "Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM." Journal of computing and information technology 24, br. 2 (2016): 133-148. https://doi.org/10.20532/cit.2016.1002701
Harvard
Ikram, S.T., i Cherukuri, A.K. (2016). 'Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM', Journal of computing and information technology, 24(2), str. 133-148. https://doi.org/10.20532/cit.2016.1002701
Vancouver
Ikram ST, Cherukuri AK. Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM. Journal of computing and information technology [Internet]. 2016 [pristupljeno 24.02.2020.];24(2):133-148. https://doi.org/10.20532/cit.2016.1002701
IEEE
S.T. Ikram i A.K. Cherukuri, "Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM", Journal of computing and information technology, vol.24, br. 2, str. 133-148, 2016. [Online]. https://doi.org/10.20532/cit.2016.1002701

Sažetak
Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS) such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA) and support vector machine (SVM). The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C) and kernel parameter gamma (γ), thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

Ključne riječi
cross validation; dimensionality reduction; intrusion detection system; principal component analysis; radial basis function kernel; support vector machine

Hrčak ID: 161730

URI
https://hrcak.srce.hr/161730

Posjeta: 995 *