The The rapid propagation of many Cyberattacks presents a serious danger to the flexibility of Software-Defined Networking (SDN). The Cyberattacks such as Botnet, Distributed Denial of Service (DDoS), Probe, Brute-Force-Attack (BFA), User-to-Root attack (U2R), Web attack and many more are disrupting the emerging technologies. The selection of a dataset for the Intrusion Detection System (IDS) is therefore crucial in achieving a comprehensive awareness of the threat environment, which is necessary to mitigate these sophisticated attacks. In this study, technical details of the properties of the data set that are essential to create efficient cyber attack mitigation techniques in SDN are covered. In the light of the critical role that datasets play in comprehending, modeling, and reducing cyberattacks, this study carefully examines selected existing datasets to determine and evaluate their applicability for cyberattacks in SDN. The insights into the characteristics of ten common datasets are covered. Datasets analysis for SDN-based cyberattacks is accessed. The statistical analysis of the InSDN dataset is evaluated to provide a solid foundation for developing better cyberattack mitigation techniques in SDN. Dataset preparation is done using Python libraries like Scikit-learn and Pandas, ensuring standardized and efficient data processing. The implementation uses Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithms, with preprocessing steps including feature scaling and class balancing to improve model performance. Finally, accuracy, precision, recall, and F1-score are measured to evaluate classification effectiveness.