Intrusion Detection System with Ensemble Machine Learning Approaches using VotingClassifier

Internets have become a part of our everyday life due to the advancement in the electronics and signal processing technologies during past decades. The tremendous growth of internet leads towards the network threats. Many times firewalls and anti-viruses fails to manage the network because of this Intrusion Detection System (IDS) comes to assists us. In this paper we use IDS with Ensemble methodologies utilized in machine learning involve the fusion of multiple classifiers to improve predictive performance, while voting classifiers combine predictions from individual models to reach conclusive decisions. The paper employs a voting ensemble method combing decision tree, logistic regression and support vector machine classifier models. We test our proposedmodel to classify the NSL-KDD dataset. Our ensemble methodologies of proposed algorithmproduce a good result.