Computer network intrusion detection using various classifiers and ensemble learning

Abstract

In this paper, we execute anomaly detection over the computer networks using various machine learning algorithms. We then combine these algorithms to boost the overall performance. We implement three different types of classifiers, i.e, neural networks, decision trees and logistic regression. We then boost the overall performance of the intrusion detection algorithm using ensemble learning. In ensemble learning, we employ weighted majority voting scheme based on the individual classifier performance. We demonstrate a significant increase in the accuracy through a set of experiments KDD Cup 99 data set for computer network intrusion detection.