A novel ensemble learning-based model for network intrusion detection

Abstract

The growth of Internet and the services provided by it has been growing exponentially in the past few decades. With such growth, there is also an ever-increasing threat to the security of networks. Several efficient countermeasures have been placed to deal with these threats in the network, such as the intrusion detection system (IDS). This paper proposes an ensemble learning-based method for building an intrusion detection model. The model proposed in this paper has relatively better overall performance than its individual classifiers. This ensemble model is constructed using lightweight machine learning models, i.e., Gaussian naive Bayes, logistic regression and decision tree as the base classifier and stochastic gradient descent as the meta-classifier. The performance of this proposed model and the individual classifiers used to build the ensemble model is trained and evaluated using three datasets, namely, KDD Cup 1999, UNSW-NB15 and CIC-IDS2017. The performance is evaluated for binary class as well as multiclass classifications. The proposed method also incorporates the usage of a feature selection method called Chi-square test to select only the most relevant features. The empirical results definitively prove that using an ensemble classifier can be immensely helpful in the field of intrusion detection system with unbalanced datasets where misclassifications can be costly.