Strengthening Cybersecurity: DDoS Attack Detection with Deep Learning and Innovative Hybrid Methods

Campoverde J.C.; Campoverde M.C.; Campoverde D.C.; Campoverde N.C.; Diaz J.C. · Indonesian Journal of Electrical Engineering and Informatics (2025) · DOI: 10.52549/ijeei.v13i4.7268

Resumen

Distributed Denial-of-Service (DDoS) attacks continue to disrupt the availability of online services, motivating the development of robust and scalable detection mechanisms. This work proposes a hybrid CNN–LSTM detection framework evaluated in a controlled, sandboxed testbed for traffic generation and monitoring. The framework is implemented under a supervised learning setting and is positioned to incorporate semi-supervised and transfer learning strategies to address label scarcity and potential distribution shift in future extensions. Using a dataset of 6,000 labeled traffic logs and an 80/10/10 train/validation/test split, the proposed model achieves 98.67% accuracy, 98.01% precision, 96.73% recall, and 97.37% F1-score, outperforming Random Forest (96.42%) and a standalone LSTM (97.10%). Overall, the hybrid design supports improved detection robustness and can serve as a practical component within layered DDoS defense strategies (e.g., filtering and elastic scaling) in operational environments. Copyright

Instituciones: Universidad Peruana de Ciencias Aplicadas; Universidad de Lima