Machine Learning based Intrusion Detection Framework using Recursive Feature Elimination Method

Abstract

Intrusion detection has a prominent part for ensuring the information security. Machine learning approaches are followed to detect intrusion or anomaly of a network. The network traffic produce large amount of data, the Analyzing and monitoring the data is the biggest challenge here. To overcome that feature elimination or selection is done before classification. The dataset has some features which are irrelevant which makes the detection process slower and degrades the system performance. In order to improve the performance, this system identifies the features which are irrelevant and eliminated it. The feature selection is achieved by using Recursive Feature elimination method. For the selected feature classification is done by using classification model. The proposed system use KDD CUP 99 dataset. In this system four classifier models such as LDA, SVMr, Random forest and Adaboost are used, among that the Adaboost gives 99.75 % sensitivity and 95.69 % specificity which are higher when compared to other classifier. Using this system unknown future attacks can also be detected.