DDoSGedik30K: A Unique Dataset and Advanced Deep Learning Techniques for DDoS Attack Detection

The rapid advancements in network technologies, along with the increasing volume and scope of data transmitted over networks, have led to a rise in both the intensity and complexity of cyber threats and attacks. One of the most prominent and destructive types of cyberattacks threatening network and system security is the Distributed Denial of Service (DDoS) attack. This study examines the use of deep learning techniques to develop an effective detection mechanism against the growing number of DDoS attacks today. For this purpose, a dataset called DDoSGedik30K, which includes real-world attack scenarios, was created. Using this dataset, a total of 12 models were developed based on Feedforward Neural Network (FFNN) and Long Short-Term Memory (LSTM) deep learning architectures. The fact that all models exceeded a 99.9% accuracy rate proves that the proposed dataset is highly effective in detecting DDoS attacks. Additionally, cross-validation (CV) was employed in the model evaluation, and the experimental results are consistent with the obtained performance metrics, demonstrating the reliability and robustness of the proposed approach. This study introduces the DDoSGedik30K dataset and deep learning models that significantly improve DDoS attack detection. These contributions enhance cybersecurity research and provide actionable insights for future advancements.