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Intruder is one of the most publicized threats to security. In recent years, intrusion detection has emerged as an important technique for network security. Data mining techniques have been applied as a new approach for intrusion detection. The quality of the feature selection methods is one of the important factors that affect the effectiveness of Intrusion Detection system (IDS). This paper evaluates the performance of data mining classification algorithms namely J48, Naive Bayes, NBTree and Random Forest using KDD CUP'99 dataset and focuses on Correlation Feature Selection (CFS) measure. The results show that NBTree and Random Forest outperforms other two algorithms in terms of predictive accuracy and detection rate.