Abstract:
Now-a-days, Internet is playing vital role to change economic, political and social structure positively. In business transaction,
the enormous assistance of Internet have stemmed in increased number of users and subsequently intruders. Intrusion Detection System
detects intruders in networks. Using traditional approaches of intrusion detection, it is actual difficult to analyze packets in network.
Development of Intrusion detection system by using ensemble method is leading to faster and enhance accurate detection rate. In this
paper, decision trees based an ensemble classifier has proposed for detection of intrusion in network. The key aim of this research is to
develop an ensemble to enhance accuracy of network intrusion detection on testing data set. Three decision trees have used as a base
classifier. Because the decision trees are modest in environment and produce simple rules in if-then form. For building and testing the
proposed ensemble based classifier, NSL-KDD dataset have used. The novelty of this research work is that ensemble of fast decision
trees have combined together which provided very high accuracy. Experimental results shows that the proposed ensemble classifier beats
its base classifiers and other existing ensemble classifiers on test dataset. It is also observed that the proposed ensemble classifier offers
improved classification accuracy than Random forest and AdaBoost on test data-set. The proposed ensemble classifier also offers better
accuracy than existing classifiers on training data-set. The proposed ensemble classifier also provide higher accuracy than classifier
proposed in literature on 10-fold cross validation. Overall, the proposed ensemble based classifier beats standard ensemble classifiers
and existing classifiers.
