Intrusion Detection System using Ensemble of Decision Trees and Genetic Search Algorithm as a Feature Selector

Year 2020, Volume: 9 Issue: 2, 104 - 113, 01.06.2020

D P Gaikwad

Abstract

In the middle of this wonderful Internet technology, the rise and growth of Internet misuse is shocking which compromises the security of the computers in network. In doing so, the use of the Internet becomes very destructive for one and all. Any unauthorized person can steal private information by hacking computer. Anonymous attack has many causes, such as viruses, malware, misuse of privileges on the computer and unauthorized access to information systems. To reduce the exposure to such types of threats, organizations need a reliable, robust and fast computer network security mechanism. Intrusion detection is a mechanism which detects and prevents different intruders in internet. There are many techniques of machine learning which can to apply intrusion detection systems. Current, many researcher are using ensemble methof to implement IDS. The selection of base classifeirs In ensemble method, the selection of suitable selection of base classifiers is a very key process. This paper propose a novel intrusion detection systems using ensemble of two well-known decision trees. C4.5 decision tree and Random Forest have selected as a base classifiers. Intrusion detection system is framed by combining the gains of both C4.5 and Random Forest decision trees. The working of the proposed ensemble for intrusion detection system has estimated in terms of classification accuracy, true positives and false positives. The experimental results show that the offered ensemble classifier for intrusion detection performs well in classification accuracy, true positive than individual decision trees on testing dataset. Other aspects of performance of classifiers are described in the paper.

Keywords

Ensemble classifier, Random Forest, C4.5, Classification Accuracy, False Positives

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