Intrusion Detection in Computer Networks using Optimized Machine Learning Algorithms

Abstract

Intrusion detection systems (IDSs) are employed to maintain computer networks from cyber attacks. Here, the aim is to detect intrusions once the data is transmitted across the internet. Intrusion detection methods (IDMs) developed in the literature are commonly focused on data mining, statistical and machine learning-based analysis of anomaly situations. In this paper, we suggested machine learning based methodology to detect intrusions in computer networks. The proposed method comprises four main phases, namely, preprocessing, feature selection, parameter optimization and classification. Most significant features are selected by utilizing the Correlation Based Feature Selection. For classification Random Tree, AdaBoost, K-Nearest Neighbor (KNN), Support-vector machine (SVM) are used while for parameter optimization particle swarm optimization is employed. The proposed method was tested on two extensive datasets, namely NSL-KDD and CIC-DDOS2019. The experimental results reveal that the proposed method can effectively classify intrusions with a high detection rate and that it outperforms the other machine learning techniques.