Intrusion detection systems (IDS) play a major role in detecting the attacks that occur in the computer or networks. Anomaly intrusion detection models detect new attacks by observing the deviation from profile. However there are many problems in the traditional IDS such as high false alarm rate, low detection capability against new network attacks and insufficient analysis capacity. The use of machine learning for intrusion models automatically increases the performance with an improved experience. This paper proposes a novel method of integrating principal component analysis (PCA) and support vector machine (SVM) by optimizing the kernel parameters using automatic parameter selection technique. This technique reduces the training and testing time to identify intrusions thereby improving the accuracy. The proposed method was tested on KDD data set. The datasets were carefully divided into training and testing considering the minority attacks such as U2R and R2L to be present in the testing set to identify the occurrence of unknown attack. The results indicate that the proposed method is successful in identifying intrusions. The experimental results show that the classification accuracy of the proposed method outperforms other classification techniques using SVM as the classifier and other dimensionality reduction or feature selection techniques. Minimum resources are consumed as the classifier input requires reduced feature set and thereby minimizing training and testing overhead time. © 2014 IEEE.