Optimal feature selection in intrusion detection using SVM-CA

Feature selection plays a vital role in toning down the effects of the curse of dimensionality in the humungous datasets seen in intrusion detection. Feature selection algorithms are used to pick the relevant features and averts the extraneous and repeated features from the dataset to improve the efficiency. It can reduce processing time, dimension of data and enhance the performance of the system in terms of precision and training time. This paper proposes a novel variant of support vector machine, known as SVM correlation algorithm (SVM-CA) to choose the relevant features. The combination of SVM with correlation algorithm enhances the classification accuracy. Our proposed SVM-CA algorithm deals with the problems faced by the existing algorithm like low accuracy and high detection time. The performance of the algorithm is appraised by five parameters including the modelling time, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental results show that our proposed technique decreases the false positive rate and processing time.