An Optimized Decision Tree Approach for Intrusion Detection
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Assistant Professor, Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
Publication date: 2019-03-16
Eurasian J Anal Chem 2019;14(1):emEJAC191021
Nowadays, with rapid development in networking infrastructures and with an increase in Internet usage, network security has become an important issue for discussion. Some major challenges with regard to network security are DOS attack, Botnets etc., and sometimes vulnerabilities in network design can also serve as intrusion points for intruders. Therefore, this paper focuses and ensures on optimum network security by setting some thresholds on generic based feature selection mechanism in order to block and overcome attacks like DOS, R2L and U2R etc. In order to verify our approach, a broadly known intrusion dataset named NSL-KDD is used. For detecting the attacks in a network efficiently and also to reduce the false alarm rate, we optimize the decision trees by using Ant Colony Optimization (ACO) algorithm. In order to reduce the dataset size we have used ACO algorithm for feature selection. This would provide a more efficient and reduced version of a decision tree and it will also help to identify the exact attack categories. Thus, this approach will prove to be quite an efficient way to identify intrusions in a network for the detection of any abnormal activity on the network .Thus, the proposed system will (1) immediately block an intruder if any of the threshold values set are exceeded. (2) it will list the exact type of attack used by an intruder to get access to the network (3) it also ensures optimum network security.