Hybrid deep CNN RNN model for securing IOT against DDOS attacks

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.