Intrusion Detection System Using Bagging Ensemble Method of Machine Learning

Intrusion detection system is widely used to protect and reduce damage to information system. It protects virtual and physical computer networks against threats and vulnerabilities. Presently, machine learning techniques are widely extended to implement effective intrusion detection system. Neural network, statistical models, rule learning, and ensemble methods are some of the kinds of machine learning methods for intrusion detection. Among them, ensemble methods of machine learning are known for good performance in learning process. Investigation of appropriate ensemble method is essential for building effective intrusion detection system. In this paper, a novel intrusion detection technique based on ensemble method of machine learning is proposed. The Bagging method of ensemble with REPTree as base class is used to implement intrusion detection system. The relevant features from NSL_KDD dataset are selected to improve the classification accuracy and reduce the false positive rate. The performance of proposed ensemble method is evaluated in term of classification accuracy, model building time and False Positives. The experimental results show that the Bagging ensemble with REPTree base class exhibits highest classification accuracy. One advantage of using Bagging method is that it takes less time to build the model. The proposed ensemble method provides competitively low false positives compared with other machine learning techniques.