How can I address data imbalance issues in a DDoS detection dataset?

Data imbalance, where attack traffic is much less common than normal traffic, can be addressed through several techniques. These include oversampling the minority class (e.g., using SMOTE to generate synthetic attack samples), undersampling the majority class (e.g., randomly removing normal traffic samples), and using cost-sensitive learning algorithms that penalize misclassification of the minority class more heavily. The specific approach should be chosen based on the characteristics of the dataset and the performance goals.