Hybrid deep CNN RNN model for securing IOT against DDOS attacks

Jawad, Ameer Ali and Ghandour, Ahmad and Al-Salih, Asaad Abdul Malik Madhloom and Majeed, Mohammed Abdul and Ahmed, Mahmood Anees and Khalaf, Bashar Ahmed and Ibraheem, Ibraheem Kasim and Azar, Ahmad Taher and Humaidi, Amjad J. and Msallam, Mohammed Majid (2026) Hybrid deep CNN RNN model for securing IOT against DDOS attacks. POLLACK PERIODICA: AN INTERNATIONAL JOURNAL FOR ENGINEERING AND INFORMATION SCIENCES, 21 (1). pp. 113-120. ISSN 1788-1994

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Abstract

The rapid expansion of Internet of things networks has increased vulnerability to distributed denial of service attacks. This paper proposes a hybrid deep learning model that combines convolutional neural networks and recurrent neural networks for effective distributed denial of service detection in Internet of things environments. The model leverages convolutional neural networks for feature extraction and recurrent neural networks for temporal modeling to classify benign, light, and heavy attack traffic. Evaluated using the CIC-Bell-DNS-EXF2021 dataset, it achieved 99.5% accuracy, 99.9% precision, and 99.6% F1 score, outperforming traditional machine learning methods and enhancing real-time security.

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