Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis
The progress of contemporary technology has rendered information systems essential in our everyday existence, underscoring the crucial necessity to safeguard information security and privacy. In password authentication, the Electronic Personal Synthesis Behaviour (EPSB) heightens the accuracy of aut...
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Ital Publication
2025-02-01
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| Series: | Emerging Science Journal |
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| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2827 |
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| author | Mohanaad Shakir |
| author_facet | Mohanaad Shakir |
| author_sort | Mohanaad Shakir |
| collection | DOAJ |
| description | The progress of contemporary technology has rendered information systems essential in our everyday existence, underscoring the crucial necessity to safeguard information security and privacy. In password authentication, the Electronic Personal Synthesis Behaviour (EPSB) heightens the accuracy of authorizing an authenticated user based on three parameters: EPSBERROR, EPSBTime, and EPSBStyle. EPSBTime suffers from a lack of indicators associated with the legitimate user; containing only six indicators, there arose the need to adopt methods for generating additional reliable indicators by analyzing old indicators and generating new indicators related to the legitimate user. Therefore, this study aims to test the impact of adopting time series analysis in the EPSB time indicator on improving the differentiation of user legitimacy in the case of password-stolen attacks. The research methodology, which involves analyzing and evaluating existing authentication methods in web-based systems, is a key component of this study. The study is divided into stages, with the first phase focusing on enhancing the existing EPSB model, the second phase implementing EPSBalgorithmV01, and the final stage ensuring validation. Thus, two preliminary experiments were conducted with 22 users from January 13 to February 1, 2024. The final phase involved comparing EPSBV01's accuracy in determining unauthorized users before and after using the ARIMA method. Thus, the EPSBV01algorithm successfully identified 17 unauthorized users during a stolen password attack simulation, outperforming the normal EPSB by 22.73%.
Doi: 10.28991/ESJ-2025-09-01-014
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| format | Article |
| id | doaj-art-c2799058732946e6b77a992c51224b33 |
| institution | Kabale University |
| issn | 2610-9182 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-c2799058732946e6b77a992c51224b332025-02-08T14:26:27ZengItal PublicationEmerging Science Journal2610-91822025-02-019124326010.28991/ESJ-2025-09-01-014774Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series AnalysisMohanaad Shakir0Management Information System (MIS), College of Business (CoB), University of Buraimi (UoB), Buraimi,The progress of contemporary technology has rendered information systems essential in our everyday existence, underscoring the crucial necessity to safeguard information security and privacy. In password authentication, the Electronic Personal Synthesis Behaviour (EPSB) heightens the accuracy of authorizing an authenticated user based on three parameters: EPSBERROR, EPSBTime, and EPSBStyle. EPSBTime suffers from a lack of indicators associated with the legitimate user; containing only six indicators, there arose the need to adopt methods for generating additional reliable indicators by analyzing old indicators and generating new indicators related to the legitimate user. Therefore, this study aims to test the impact of adopting time series analysis in the EPSB time indicator on improving the differentiation of user legitimacy in the case of password-stolen attacks. The research methodology, which involves analyzing and evaluating existing authentication methods in web-based systems, is a key component of this study. The study is divided into stages, with the first phase focusing on enhancing the existing EPSB model, the second phase implementing EPSBalgorithmV01, and the final stage ensuring validation. Thus, two preliminary experiments were conducted with 22 users from January 13 to February 1, 2024. The final phase involved comparing EPSBV01's accuracy in determining unauthorized users before and after using the ARIMA method. Thus, the EPSBV01algorithm successfully identified 17 unauthorized users during a stolen password attack simulation, outperforming the normal EPSB by 22.73%. Doi: 10.28991/ESJ-2025-09-01-014 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2827authenticationintelligent authenticationepsbarimamfatime series analysiscybersecurityaistolen password attackimplicit authenticationhuman behavior recognition. |
| spellingShingle | Mohanaad Shakir Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis Emerging Science Journal authentication intelligent authentication epsb arima mfa time series analysis cybersecurity ai stolen password attack implicit authentication human behavior recognition. |
| title | Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis |
| title_full | Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis |
| title_fullStr | Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis |
| title_full_unstemmed | Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis |
| title_short | Enhancing User Differentiation in the Electronic Personal Synthesis Behavior (EPSBV01) Algorithm by Adopting the Time Series Analysis |
| title_sort | enhancing user differentiation in the electronic personal synthesis behavior epsbv01 algorithm by adopting the time series analysis |
| topic | authentication intelligent authentication epsb arima mfa time series analysis cybersecurity ai stolen password attack implicit authentication human behavior recognition. |
| url | https://ijournalse.org/index.php/ESJ/article/view/2827 |
| work_keys_str_mv | AT mohanaadshakir enhancinguserdifferentiationintheelectronicpersonalsynthesisbehaviorepsbv01algorithmbyadoptingthetimeseriesanalysis |