A Comprehensive Study of Deep Learning Models for Intrusion Detection in IoT Devices - Khairullah, Enas F.; Alsenani, Nibras (Engineering, Technology & Applied Science Research, 2025)
The Internet of Things (IoT) has revolutionized how people interact with the world, but the increasing complexity of cyberattacks poses significant challenges in detecting intrusions. Failure to prevent intrusions can compromise IoT security services, including data confidentiality, integrity, and availability. For this reason, this study employs four deep learning models: A Deep Neural Networks (DNN), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Long-Short-Term Memory (LSTM) network. The multiclassification performance of each model was evaluated using the Bot-IoT dataset. This study also addresses the bias towards the DDoS/DoS category in the Bot-IoT dataset, using the SMOTE technique to mitigate overfitting. The LSTM model achieved an excellent balance between performance and efficiency, outperforming state-of-the-art deep learning Intrusion Detection System (IDS) approaches on the same dataset, achieving a multiclass classification accuracy of 99.97%.