Ensemble of Learner for Network Intrusion Detection System

Abstract

The uses of the internet have improved drastically for online communication and working from home. Data sharing and integration of global information bring network security risks. To protect private data and information, network security is becoming a very important research topic. An intrusion detection system is generally used as safe operational tool. It excellently detects and prevents intruders in a network by issuing a warning before the attack is launched in a network. An ensemble technique is extensively used to employ intrusion detection systems. In this paper, the stacking method of the ensemble has been proposed for the intrusion detection system. Three base classifiers have been stacked using the Meta classifier. Multilayer Perceptron, Ripple Down Rule learner, and RepTree Decision Tree have been used as Base Classifiers. These Base Classifiers are stacked using Logistic Regression with ridge estimator Meta Classifier. These learners have been trained and tested using the NSL dataset. A genetic algorithm has been applied for choosing relevant and most corrected features which helped in reducing the dimension of the dataset. Experimental results demonstrate that the proposed stacked classifier gives accuracies of 79.36%, 99.72%, and 99.64% on a test, train dataset, and cross-validation respectively. It is observed that the proposed stacked classifiers do better than existing hybrid intrusion detection systems.