Machine Learning-Based Network Intrusion Detection Optimization for Cloud Computing Environments

Abstract

Cloud computing is an emerging choice among businesses all over the world since it provides flexible and world wide Web computer capabilities as a customizable service. Because of the dispersed nature of cloud services, security is a major problem. Since it is extremely accessible to intruders for any kind of assault, privacy and security are major hurdles to the on-demand service’s success. A massive increase in network traffic has opened the path for increasingly difficult and broad security vulnerabilities. The use of traditional Intrusion Detection Systems (IDS) to prevent these attempts has proven ineffective. Therefore, this paper proposes a novel Network Intrusion Detection System (NIDS) based on a Machine Learning (ML) model known as the Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) techniques. Furthermore, the hyperparameter optimization technique based on the Crow Search Algorithm is being utilized to optimize the NIDS’ performance. Besides, the XGBoost-based feature selection technique is used to improve the classification accuracy of NIDS’s method. Finally, the performance of the proposed system is evaluated using the NSL-KDD and UNR-IDD datasets, and the experiment results show that it performs better than baselines and has the potential to be used in modern NIDS.