A Comparative Study of Oversampling Techniques for Imbalanced Network Attack Detection

Class imbalance remains a pressing issue in machine learning, particularly when advanced technologies require it from machine learning implementations. Medical or cybersecurity applications are good examples of this, as well as others. In this paper, we focus on cybersecurity, where the minority class often represents rare but critical attack instances. We present a comparative study of various oversampling strategies—including SMOTE-based methods, GAN-based methods, and hybrid approaches—for detecting network intrusions in imbalanced datasets. Selected classifiers are evaluated using metrics such as Accuracy, AUC, F1-score (for the minority class), and Recall. The experimental results are analysed and discussed, in terms of the strengths and weaknesses of each oversampling strategy.