Evaluation of Efficient Classification Algorithm for Intrusion Detection System

Intrusion detection system is one of the most significant network security problems in the technology world. To improve the Intrusion Detection System (IDS) many machine learning methods are implemented. In order to develop the performance of IDS, different classification algorithms are applied to detect different types of attacks. For building efficient IDS is not an easy task and choosing a suitable classification algorithm. The best method is to test the Performance of the different classification algorithms and select best method from them. This paper aim is to assemble an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system.