Addressing Class Imbalance in Intrusion Detection: A Comprehensive Evaluation of Machine Learning Approaches

The ever-growing number of cyber attacks in today’s digitally interconnected world requires highly efficient intrusion detection systems (IDSs), which accurately identify both frequent and rare network intrusions. One of the most important challenges in IDSs is the class imbalance problem of network traffic flow data, where benign traffic flow significantly outweighs attack instances. This directly affects the ability of machine learning models to identify minority class threats. This paper is intended to evaluate various machine learning algorithms under different levels of class imbalances, using resampling as a strategy for this problem. The paper will provide an experimental comparison by combining various algorithms for classification and class imbalance learning, assessing the performance through the F1-score and geometric mean (G-mean). The work will contribute to creating robust and adaptive IDS through the judicious integration of resampling with machine learning models, thus helping the domain of cybersecurity to become resilient.