A divide and conquer feature reduction and feature selection algorithm in KDD intrusion detection dataset

Generally, an IDS [10] is responsible to find out the attack and normal behavior analyzing intrusion detection datasets. But sometimes the attacker intelligence causes fail to identify the attack. Because attacker having various methods to attack which cannot be every time identifiable by an IDS. This paper provides a divide and conquers feature reducing and feature selection algorithm to reduce the feature set from a large KDD 99 dataset. Then reduced feature sets are classified on a KDD dataset with the help of the Tanagra data mining tool. So, the proposed algorithm is selected important feature set and classified with maximized classification rate.