This paper evaluates the effectiveness of deep learning models (DNNs, CNNs, and RNNs) in detecting cyberattacks in IoT environments using the CICIoT2023 dataset. The study aims to address the growing threat of cyberattacks on IoT devices, which are increasingly vulnerable due to their low computational and storage capabilities. The proposed method includes data preprocessing, robust scalar and label encoding techniques, and model prediction using deep learning models. The experimental results show that the RNN model achieved the highest accuracy of 96.56%, outperforming other models such as DNNs and CNNs. The study contributes to the development of an effective cyberattack detection system in IoT environments, providing a robust solution for mitigating cyber threats. The findings suggest that deep learning models can be a valuable tool for enhancing the security of IoT networks.This paper evaluates the effectiveness of deep learning models (DNNs, CNNs, and RNNs) in detecting cyberattacks in IoT environments using the CICIoT2023 dataset. The study aims to address the growing threat of cyberattacks on IoT devices, which are increasingly vulnerable due to their low computational and storage capabilities. The proposed method includes data preprocessing, robust scalar and label encoding techniques, and model prediction using deep learning models. The experimental results show that the RNN model achieved the highest accuracy of 96.56%, outperforming other models such as DNNs and CNNs. The study contributes to the development of an effective cyberattack detection system in IoT environments, providing a robust solution for mitigating cyber threats. The findings suggest that deep learning models can be a valuable tool for enhancing the security of IoT networks.