- Volume 18
- Issue 1
- (3) Articles
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)
Mahmoud A. Zaher
,
Nabil M. Eldakhly
Email has been a key communication and information-management tool in contemporary organizations, yet it is also one of the most misused avenues to spam, fraud, credential theft, and malicious code delivery. Lightweight and reproducible detection models are especially useful to universities, public institutions, and small-to-medium enterprises which might not have access to costly proprietary filtering infrastructures because of the operational relevance of email security. In this paper I suggest an Explainable Hybrid SVM Framework (EHSF) to detect spam and malicious-risk email in a business information system. The framework integrates TF–IDF representation of text with lightweight risk-based email indicators, such as structural and lexical cues that can be obtained at low computation cost. An external Enron- Spam data were used so that it may be reproducible and will be checked later by the reviewers and readers. The experimentation process was coded in Python and assessed in terms of accuracy, precision, recall, F1-score, ROC-AUC, and confusion-matrix. These findings demonstrate that the suggested Linear SVM-based framework has the highest overall performance with accuracy of 0.9853, precision of 0.9818, recall of 0.9893, F1-score of 0.9855, and ROC-AUC of 0.9981 on the held-out test set. The confusion matrix shows that there were only 34 false negatives and 58 false positives which show that there was a good discrimination between ham and spam classes. Besides the predictive performance, the framework provides an interpretable layer based on the analysis of influential lexical indicators related to risky and legitimate enterprise emails. The research adds a replicable and operationally viable methodology that complies with the needs of cybersecurity and information-management, and is lightweight enough to be implemented in the real-life setting within an organization.
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Doi: https://doi.org/10.54216/JCIM.180103
Vol. 18
Issue. 1
PP. 45-55, (2026)
