Feature Selection and Fuzzy Decision Tree for Network Intrusion Detection

Extra features can increase computation time, and can impact the accuracy of the Intrusion Detection System. Feature selection improves classi cation by searching for the subset of features, which best classify the training data. This paper proposed approach uses Mutual Correlation for feature selection which reduces from 34 continuous attributes to 10 continuous attributes and Fuzzy Decision Tree for detection and diagnosis of attacks. Experimental results on the 10% KDD Cup 99 benchmark network intrusion detection dataset demonstrate that the proposed learning algorithm achieved high true positive rate (TPR) and significant reduce false positive rate (FP ).