Using Decision Tree Classifiers for Efficient Intrusion Detection System
Maximum processing computation and more time consuming task has always been a limit in processing huge network intrusion data. This problem can be minimized through feature selection to condense the size of the network data involved. In this paper, the authors first preprocess dataset KDD 99 cup. Then they study and analysis of two decision tree algorithms (C4.5 and standard ID3) of data mining for the task of detecting intrusions and compare their relative performances. Based on this paper, it can be concluded that C4.5 decision tree is the most suitable with high True Positive Rate (TPR) and low False Positive Rate (FTR) and low computation time with high accuracy.