@article{article_1709122, title={DDoSGedik30K: A Unique Dataset and Advanced Deep Learning Techniques for DDoS Attack Detection}, journal={International Journal of New Findings in Engineering, Science and Technology}, volume={3}, pages={86–102}, year={2025}, DOI={10.61150/ijonfest.2025030201}, author={Kocakoyun Aydoğan, Şenay and Pura, Turgut and Çıplak, Zeki and Yıldız, Anıl}, keywords={DDoS Algılama, Derin Öğrenme, DDoS Veri Seti, DDoSGedik30K, DDoS Analizi}, abstract={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 achieved a 99.9% accuracy rate proves that the proposed dataset is highly effective in detecting DDoS attacks. The dataset and the optimized deep learning models for DDoS attack detection proposed in this study provide a significant contribution to the literature and offer findings that could guide future cybersecurity research.}, number={2}, publisher={İstanbul Gedik Üniversitesi}, organization={Istanbul Gedik University}