Enhancing Intrusion Detection Systems Using Machine Learning Classifiers on the CSE-CIC-IDS2018 Dataset
Khalil Ibrahimi, Mohammed Jouhari, Zineb Jakout · 2024
With the rapid growth in the Internet of Things (IoT), current cybersecurity threats are growing to levels at which traditional intrusion detection systems (IDSs) cannot suffice. The purpose of this paper, therefore, is to evaluate the efficacy of ML techniques in enhancing IDS to adapt to emerging sophisticated and dynamic modern cyber threats. We adopted four supervised ML models: Decision Tree, Random Forest, Naive Bayes, and Gradient Boost, all of which describe the CSE-CIC-IDS2018 dataset representing different network attack situations. This reflects today's cyber threats. We have analyzed both binary and multiclass classification tasks to understand what kind of cyber threat and how many any model was best suitable for. This implies that ML-supported IDS can effectively improve the detection of not only generic but also specific cyber threats, therefore enhancing security. It is in line with this that the current paper outlines the strengths and weaknesses of the discussed models to produce an insight and judgment of their practical implementation in real-world scalability. The outcomes came into view that ML-based IDS gives resilient, adaptable, and proactive solutions to cybersecurity over IoT networks.