Feature selection based intrusion detection system using the combination of DBSCAN, K-Mean++ and SMO algorithms

IDS is the main concern of the security which is useful to prevent the attack at host and network level. In this propose work, classification of KDD intrusion dataset is proposed along with noise reduction, clustering and feature selection. DBSCAN algorithm has been applied to reduce noise present in KDD dataset. After noise removal genetic search approach is utilize to pick relevant feature. K-Means++ clustering method is utilized to cluster the dataset and resultant dataset is tested by SMO based classifier. A confirmation of concept prototype has been implemented to examine the performance of proposed approach using WEKA and MATLAB data mining tools. It is observed that proposed methods gives 96.922% accuracy. A comparative analysis performed between proposed methods and KMSVM (Simple K-mean with SVM classification) and it is observed that proposed method gives better results.