Modified Mutual Information-based Feature Selection for Intrusion Detection Systems in Decision Tree Learning
As network-based technologies become omnipresent, intrusion detection and prevention for these systems become increasingly important. This paper proposed a Modified Mutual Information-based Feature Selection algorithm (MMIFS) for intrusion detection on the KDD Cup 99 dataset. The C4.5 classification method was used with this feature selection method. In comparison with Dynamic Mutual Information Feature Selection algorithm (DMIFS), the people can see that most performance aspects are improved. Furthermore, this paper shows the relationship between performance, efficiency and the number of features selected.