Hybrid deep CNN RNN model for securing IOT against DDOS attacks

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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Publication Details

Journal
Pollack Periodica
Published
2025-09-19
DOI
https://doi.org/10.1556/606.2025.01271
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Hybrid deep CNN RNN model for securing IOT against DDOS attacks

Selmy Awad, Amjad J. Humaidi, Ibraheem Kasim Ibraheem, Mohammed Majid Msallam et al.
Pollack Periodica
Network Security and Intrusion Detection
article

Hybrid deep CNN RNN model for securing IOT against DDOS attacks

Selmy Awad, Amjad J. Humaidi, Ibraheem Kasim Ibraheem, Mohammed Majid Msallam, Bashar Ahmed Khalaf, Ahmad Ghandour, Asaad Abdul Malik Madhloom Al-Salih, Ahmad Taher Azar, Mahmood Anees Ahmed, Mohammed Abdul Majeed
article en

Abstract

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.

Pollack PeriodicaVol. 21(1)
University of Baghdad (IQ), Prince Sultan University (SA), University of Technology - Iraq (IQ), Benha University (EG), Baghdad Medical City (IQ), Alsalam University College (IQ), Islamic University of Lebanon (LB), University of Diyala (IQ)
Openalex Percentile: Top 71%
Network Security and Intrusion Detection
DOISourceOpenAlex
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Hybrid deep CNN RNN model for securing IOT against DDOS attacks — Selmy Awad, Amjad J. Humaidi, et al. · Pollack Periodica (2025) | TGRS Research Map | TGRS