In datasets, the preponderance of imbalanced classes impedes accurate cyberattack categorization. While high aggregate accuracy is sought, it's paramount to adeptly classify all attack types, especially the under-represented ones. Existing methodologies, such as Ensemble techniques and the Synthetic Minority Oversampling Technique (SMOTE), address these disparities, yet the dynamic nature of underrepresented cyberattacks in cybersecurity remains a concern. To address this, we introduce a nested cascade model tailored for diverse cyberattacks within imbalanced datasets. This model leverages binary classifiers across tiers, each targeting a specific attack type. Before initializing the cascade, SMOTE is applied to counterbalance class disparities. The cascade's classification sequence employs a dual strategy: an initial one-vs-all binary classifier approach for pending classes, followed by prioritization based on model performance. We assessed our approach using the UNSW-NB15 dataset. Preliminary results indicate approximately 80% efficiency across metrics like accuracy, recall, and Fl-score. Notably, SMOTE's in- tegration yielded significant improvements for underrepresented classes.