Intrusion Detection System on KDD’99 Dataset with Imbalanced Classes

Abstract

The paper describes a method of intrusion detection that keeps check of it with help of machine learning algorithms. The experiments have been conducted over KDD’99 cup dataset, which is an imbalanced dataset, cause of which recall of some classes coming drastically low as there were not enough instances of it in there. For Preprocessing of dataset One Hot Encoding and Label Encoding to make it machine readable. The dimensionality of dataset has been reduced using Principal Component Analysis and classification of dataset into classes viz. attack and normal is done by Naïve Bayes Classifier. Due to imbalanced nature, shift of focus was on recall and overall recall and compared with other models which have achieved great accuracy. Based on the results, using a self optimizing loop, model has achieved better geometric mean accuracy.