Performance Analysis of Machine Learning Techniques in Network Intrusion Detection

Abstract

A lot of sensitive data is being transmitted over the internet nowadays, which leads to increasedrisks of network attacks. To identify suspicious and malicious activities to secure internal networks,intrusion detection systems aim to recognize unusual access or attacks to the network. Machine learningtechnology can play a vital role in a scheme to detect intrusion. It is a technology that is based onclassification and prediction, to deal with security threats. In this work, we focus on significant featureselection and classification using four machine learning algorithms. Adaptive Boost (AdaBoost), GradientBoosting, Random Forest, and Decision Tree classification techniques have been tested on the dataset ofnetwork intrusion detection which is collected from Kaggle. In our analysis, Gradient Boosting outperformsconsidering the F1-score. Therefore, this machine learning technique can be utilized to implement anintelligent intrusion detection system.