Improving accuracy of intrusion detection model using PCA and optimized SVM

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 intrusion detection 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.