An efficient intrusion detection system based on support vector machines and gradually feature removal method

The efficiency of the intrusion detection is mainly depended on the dimension of data features. By using the gradually feature removal method, 19 critical features are chosen to represent for the various network visit. With the combination of clustering method, ant colony algorithm and support vector machine (SVM), an efficient and reliable classifier is developed to judge a network visit to be normal or not. Moreover, the accuracy achieves 98.6249% in 10-fold cross validation and the average Matthews correlation coefficient (MCC) achieves 0.861161.

► This paper proposes a desirable IDS model with high efficiency and accuracy.
► It formulates a pipeline of machine learning methods, including k-means algorithm, ant colony optimization (ACO) and SVM.
► The accuracy achieves 98.6249%, and the average Matthews correlation coefficient (MCC) achieves 0.861161.