When applying the proposed method to real-world, highly imbalanced ransomware datasets, several challenges may arise. One major challenge is the skewed distribution of classes, where the number of ransomware instances significantly outweighs the normal instances. This imbalance can lead to biased model training and inaccurate detection results, as the model may prioritize the majority class and overlook the minority class. Addressing class imbalance requires careful data preprocessing techniques such as oversampling, undersampling, or using advanced algorithms like SMOTE to generate synthetic samples for the minority class. Furthermore, the performance evaluation metrics need to be chosen wisely to account for the class imbalance, such as using precision, recall, F1-score, or ROC-AUC instead of accuracy. Ensuring the model's ability to generalize well on unseen imbalanced data is crucial, and robust validation strategies like cross-validation can help mitigate the impact of class imbalance during model training and evaluation.