Evaluating Cybersecurity Awareness in Employees Using Gameplay: Data and Machine Learning Models.
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| Title: | Evaluating Cybersecurity Awareness in Employees Using Gameplay: Data and Machine Learning Models. |
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| Authors: | Nkongolo, Mike Wa1 mike.wankongolo@up.ac.za, Tokmak, Mahmut2 mahmuttokmak@mehmetakif.edu.tr |
| Source: | Proceedings of the European Conference on Games Based Learning. 2025, Vol. 19 Issue 2, p648-657. 10p. |
| Subject Terms: | *Machine learning, *Employee training, *Interactive learning, *Data analysis, *Educational games, Internet security, Random forest algorithms, Boosting algorithms |
| Abstract: | Cyber-attacks continue to pose persistent challenges within professional environments. Human error remains a critical vulnerability, frequently leading to security breaches through credential misuse and social engineering tactics. Traditional cybersecurity training approaches often lack effectiveness when not adapted to the dynamic threat landscape. This study presents CyberEmployee, a serious game developed to enhance cybersecurity awareness among employees through interactive learning. The objective is to assess employees' awareness levels by analysing gameplay data using machine learning techniques. Data were collected via the game's integrated scoreboard, which tracked user behaviors and performance patterns. The resulting dataset was analysed using multiple machine learning algorithms, including Random Forest, Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), and Logistic Regression. Experimental results demonstrated accuracy rates ranging from 86% to 100% and F1-scores from 75% to 100%. The highest performance--100% accuracy and 100% F1-score--was achieved using the Random Forest and XGBoost models. This analysis indicates that ensemble learning methods outperform other classifiers in employee classification. Furthermore, gameplay duration and player score were identified as key predictive features. These findings indicate the potential of serious games combined with machine learning for data-driven cybersecurity training frameworks. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the European Conference on Games Based Learning is the property of Academic Conferences & Publishing International Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 188969535 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluating Cybersecurity Awareness in Employees Using Gameplay: Data and Machine Learning Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nkongolo%2C+Mike+Wa%22">Nkongolo, Mike Wa</searchLink><relatesTo>1</relatesTo><i> mike.wankongolo@up.ac.za</i><br /><searchLink fieldCode="AR" term="%22Tokmak%2C+Mahmut%22">Tokmak, Mahmut</searchLink><relatesTo>2</relatesTo><i> mahmuttokmak@mehmetakif.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+European+Conference+on+Games+Based+Learning%22">Proceedings of the European Conference on Games Based Learning</searchLink>. 2025, Vol. 19 Issue 2, p648-657. 10p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Employee+training%22">Employee training</searchLink><br />*<searchLink fieldCode="DE" term="%22Interactive+learning%22">Interactive learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+games%22">Educational games</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cyber-attacks continue to pose persistent challenges within professional environments. Human error remains a critical vulnerability, frequently leading to security breaches through credential misuse and social engineering tactics. Traditional cybersecurity training approaches often lack effectiveness when not adapted to the dynamic threat landscape. This study presents CyberEmployee, a serious game developed to enhance cybersecurity awareness among employees through interactive learning. The objective is to assess employees' awareness levels by analysing gameplay data using machine learning techniques. Data were collected via the game's integrated scoreboard, which tracked user behaviors and performance patterns. The resulting dataset was analysed using multiple machine learning algorithms, including Random Forest, Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), and Logistic Regression. Experimental results demonstrated accuracy rates ranging from 86% to 100% and F1-scores from 75% to 100%. The highest performance--100% accuracy and 100% F1-score--was achieved using the Random Forest and XGBoost models. This analysis indicates that ensemble learning methods outperform other classifiers in employee classification. Furthermore, gameplay duration and player score were identified as key predictive features. These findings indicate the potential of serious games combined with machine learning for data-driven cybersecurity training frameworks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the European Conference on Games Based Learning is the property of Academic Conferences & Publishing International Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.34190/ecgbl.19.2.3978 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 648 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Employee training Type: general – SubjectFull: Interactive learning Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Educational games Type: general – SubjectFull: Internet security Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Boosting algorithms Type: general Titles: – TitleFull: Evaluating Cybersecurity Awareness in Employees Using Gameplay: Data and Machine Learning Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nkongolo, Mike Wa – PersonEntity: Name: NameFull: Tokmak, Mahmut IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20490992 Numbering: – Type: volume Value: 19 – Type: issue Value: 2 Titles: – TitleFull: Proceedings of the European Conference on Games Based Learning Type: main |
| ResultId | 1 |
