Volume 15 , Issue 2 , PP: 293-304, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Huda Lafta Majeed 1 , Esraa Saleh Alomari 2 , Ali Nafea Yousif 3 , Oday Ali Hassen 4 * , Saad M. Darwish 5 , Yu Yu Gromov 6
Doi: https://doi.org/10.54216/JCIM.150222
Given the growing demand for cybersecurity education, the practice of protecting network and software systems from digital and electronic attacks, investing in educational cybersecurity helps significantly reduce the risk of data breaches and protect against security breaches, and given the urgent need and growing number of students worldwide, it is also a way to connect with and between customers by building trust-based relationships, especially regarding essays. Automated Essay Scoring (AES) is a scalable solution for grading large amounts of essays with multiple uses, making it ideal for cybersecurity certification programs, online courses, and standardized tests. In the field of educational cybersecurity, automated essay scoring poses unique challenges due to specialized terminology, persistent and evolving threats. These automated scoring systems use domain-defined ontologies to grade essays but struggle to manage uncertainties, such as ambiguous language and partially valid arguments, which can influence the accuracy of their scoring. Traditional ontologies often struggle to interpret such uncertainties, leading to inconsistent results. Type 2 neutrosophic clustering (T2NS) as a novel approach introduced in this paper is combined with an automated article scoring system based on the cybersecurity learning ontology to address these challenges. The main steps include extracting concepts relevant to this research area from the articles, formalizing the cybersecurity scoring criteria as ontological rules and extending the ontology using T2NS, as well as defining membership functions to measure uncertainty and inconsistency levels. This evaluation using benchmark datasets of cybersecurity articles shows that this approach significantly enhances the scoring reliability and robustness of the approach compared to the basic AES methods.
Type-2 neutrosophic ontology , Cybersecurity Education (CE) , Foot of Uncertainty (FOU)
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