Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/4301 2019 2019 Networks and Systems Laboratory, Badji Mokhtar Annaba University Annaba, Algeria Ahmed Ahmed Embedded Systems Laboratory, Badji Mokhtar Annaba University Annaba, Algeria Zina Oudina Laboratoire de Gestion Electronique de Document – LabGED, Badji MokhtarAnnabaUniversity Annaba, Algeria Sabri Ghazi MQTT-based Internet of Things networks face major security problems because they have high-dimensional data, class imbalance, and no detection mechanisms that can be understood. This paper proposes a unified intrusion detection framework that integrates attention-based deep learning, GAN-driven data augmentation, and MDA-based feature selection (CNN-LSTM-Attention). The proposed pipeline outperforms both classical and recent state-of-the-art baselines. When tested on MQTTEEB-D, a real-world MQTT dataset with 200,000 flows, an accuracy of 99.12% and macro F1-score of 98.37 were achieved. However, the attention maps provide clear explanations for the obtained prediction, and the system performs well even against tough attacks such as SlowITe: 96–98%. Moreover, the system's very short inference time makes it possible to deploy on a real IoT gateway with limited resources. The synergistic combination of feature engineering, generative augmentation, and interpretable deep learning sets a standard for reliable and effective IoTMQTT intrusion detection. 2026 2026 01 21 10.54216/JCIM.180101 https://www.americaspg.com/articleinfo/2/show/4301