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

Authors

Josías Chávez Campoverde
University of Lima image/svg+xml
Misael Chávez Campoverde
Instituto de Educación Superior Toulouse-Lautrec
Daniel Chávez Campoverde
Centennial College image/svg+xml
Naomi Chávez Campoverde
Douglas College image/svg+xml
Jorge Chávez Díaz
Peruvian University of Applied Sciences image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v13i4.7268
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Published: 31-12-2025 • Issue: Vol. 13 No. 4: December 2025 • Section: Regular paper

Abstract

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.
Keywords: Cybersecurity; DDoS attacks; Deep learning; Hybrid neural networks; Digital infrastructure

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Author Biographies

Josías Chávez Campoverde
Pre-Bachelor in Systems Engineering from Universidad de Lima. Experienced in programming language and commercial software. Analyst in scientific research.
Misael Chávez Campoverde
Bachelor in Multimedia Audiovisual Communication (CAM) at Toulouse Lautrec. Experienced in digital marketing, photography, motion graphics in the financial, real estate and creative fields. Analyst in scientific research.
Daniel Chávez Campoverde
Bachelor's Degree in Business Administration from Universidad Tecnológica del Peru. Specialization in Business Analytics at Universidad del Pacífico. Graduate Program at Centennial College, Toronto
Naomi Chávez Campoverde
Bachelor's Degree in Business Administration from the University of Lima, with a specialization in Innovation and Technology. She furthered her academic journey by completing Post-Degree Diplomas in Data Analytics and Sales at Douglas College, Canada, where she gained in-depth knowledge of data-driven decision-making and market dynamics within the Canadian business environment.
Jorge Chávez Díaz
Ph. D in Accounting from Universidad Nacional Federico Villarreal. Master's Degree in Law, Economics, Management. Specialty: Management and Business Administration from the Université de Bordeaux, France. Master's Degree in Accounting with mention in Financial Auditing from ULADECH. Professor and Researcher at postgraduate level at UNMSM and UNFV. Winner of the National Accounting Award 2023 by the Board of Deans of Public Accountants of Peru. Financial consultant and chartered accountant.

How to Cite

[1]
J. Chávez Campoverde, M. Chávez Campoverde, D. Chávez Campoverde, N. Chávez Campoverde, and J. Chávez Díaz, “Strengthening Cybersecurity: DDoS Attack Detection with Deep Learning and Innovative Hybrid Methods”, IJEEI, vol. 13, no. 4, pp. 1121–1132, Dec. 2025, doi: 10.52549/ijeei.v13i4.7268.