(untitled) (International Journal of Engineering and Computer Science, 2017)
: This paper gives an indication of our study in building rare class prediction models for identifying known intrusions and their variations and anomaly detection schemes for detecting novel attacks whose nature is unknown. Data mining and machine learning have been subjected to general explore in intrusion detection with emphasis on improving the accuracy of detection classifier. The quality of the feature selection methods is one of the important factors that affect the effectiveness of Intrusion Detection scheme (IDS). This paper evaluates the performance of data mining classification algorithms namely C4.5, J48, Nave Bayes, NB-Tree and Random Forest using NSL KDD dataset and focuses on Correlation Feature Selection (CFS) assess. The results demonstrates that NB-Tree and Random Forest outperforms other two algorithms in terms of predictive accuracy and detection rate