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Publication Detailed Description
Journal Title
Revista Brasileira de Computação Aplicada
Year (definitive publication)
2026
Language
English
Country
Brazil
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Abstract
This study develops an enhanced anomaly detection framework that integrates state-of-the-art deep learning techniques. The system detects hate speech, malicious websites, vulnerabilities in open ports, and suspicious processes. It optimizes detection accuracy and reliability by leveraging tailored datasets, advanced model architectures, and refined preprocessing strategies. To ensure robustness, it employs comprehensive validation metrics, including the Polygon Area Metric (PAM). Empirical results show significant improvements in generalization and prediction accuracy over previous methodologies. The hate speech detection model achieved an accuracy of 88%, with precision of 75%, recall of 62%, F1 score of 62%, and ROC AUC of 92%. These results deliver a proactive monitoring solution suited for modern
enterprise security challenges.
Acknowledgements
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Keywords
Anomaly detection,Deep learning,Machine learning,Monitoring,Security
Fields of Science and Technology Classification
- Computer and Information Sciences - Natural Sciences
- Other Engineering and Technology Sciences - Engineering and Technology
- Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
Funding Records
| Funding Reference | Funding Entity |
|---|---|
| UIDP/04466/2025 | Fundação para a Ciência e a Tecnologia |
| 2023.16583.ICDT | Fundação para a Ciência e a Tecnologia |
| UID/00066/2025 | Fundação para a Ciência e a Tecnologia |
| LISBOA2030-FEDER-00816400 | Fundação para a Ciência e a Tecnologia |
| UIDB/04466/2025 | Fundação para a Ciência e a Tecnologia |
Contributions to the Sustainable Development Goals of the United Nations
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