Abstract—The internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continuously, developing flexible and adaptive security oriented approaches is a severe challenge. In this context, intrusion detection technique is a valuable technology to protect target systems and networks against malicious activities. But this system doesn’t provide the required accuracy. Thus to meet this requirement, this paper proposes an intrusion detection system as a model based on Proximal Support Vector Machines (PSVMs) implemented with various combination of basic kernel functions. PSVM is a light and simple modification of support vector machine. We have implemented PSVM for binary classification of intrusion detection data. For experimental training and testing NSLKDD dataset is preprocessed using Principle Component Analysis technique. Using proposed classification model, we have achieved up to 79% classification accuracy.
Index Terms—Intrusion detection system, kernel function, PCA, proximal support vector machine.
Rishabh Jain is Working for RKD, Indore, Madhya Pradesh, India (email: rkdrishabh@gmail.com).
Aprajita Pandey is working for Accenture India, Pune, India (e-mail: aprajitapandey@gmail.com).
Pramod Duraphe is working for Accenture India, Mumbai, India (email: slasher316@gmail.com).
Cite: Rishabh Jain, Aprajita Pandey, Pramod Duraphe, Bhawna Nigam, and Suresh Jain, "Performance Evaluation of PSVM Using Various Combination of Kernel Function for Intrusion Detection System," International Journal of Modeling and Optimization vol. 2, no. 5, pp. 613-617, 2012.
Copyright © 2008-2024. International Journal of Modeling and Optimization. All rights reserved.
E-mail: ijmo@iacsitp.com