Ahmed Dib
,
Zina Oudina
,
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 IoT/MQTT intrusion detection.
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Doi: https://doi.org/10.54216/JCIM.180101
Vol. 18
Issue. 1
PP. 01-21, (2026)
Belén Vila Osores
This article is part of an exhaustive study that aspired to determine the relationship between cybercrime and digital competence in sixth-cycle undergraduate students at a public university in Lima. The hypothesis was a sincere relationship between the two variables. The methodology applied is a quantitative, basic, correlational approach with a non-experimental cross-sectional design. The results reflected a medium positive correlation between cybercrime and digital competence, with a Kendall's Tau-b coefficient of 0.585 and a significance level of 0.000 (p < 0.05). In conclusion, it was evident that greater digital competence is associated with greater exposure to cybercrime risks, suggesting the need to implement educational strategies aimed at strengthening digital security in the university environment.
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Doi: https://doi.org/10.54216/JCIM.180102
Vol. 18
Issue. 1
PP. 22-44, (2026)
