A deep learning-based IDS for IoT: model proposal and comparative study of dataset balancing techniques
Antonio Villafranca, Kyaw Min Thant, Igor Alexander Bello Tasic, Maria‐Dolores Cano · Journal of Information and Telecommunication · 2026
Intrusion detection in Internet of Things (IoT) environments presents challenges due to the diversity of connected devices and their resource limitations. IoT networks generate complex, imbalanced traffic where benign activity predominates over attack instances. This imbalance hampers the performance of traditional intrusion detection systems, which struggle to generalize effectively. In this study, we present a deep neural network-based system that leverages advanced data balancing techniques – such as subsampling, Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Links – combined with cross-validation to enhance the model’s generalization and minimize overfitting. Evaluations on CICIDS2017, UNSW-NB15, and BoT-IoT datasets showed accuracy rates of 99.2%, 99.7%, and 99.8%, respectively. These results demonstrate that our methodology outperforms traditional models, especially in detecting minority attack classes, which were previously challenging due to data imbalance. The use of data balancing and cross-validation significantly improved model stability and sensitivity to diverse attack scenarios. Our findings suggest that incorporating these techniques can substantially enhance the security of IoT environments, providing a robust approach for differentiating between normal and malicious activities, thus contributing to more reliable and scalable intrusion detection systems.