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Shernahar K. Tahil
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Access Control Mechanisms and Their Role in Preventing Unauthorized Data Access: A Comparative Analysis of RBAC, MFA, and Strong Passwords Edrian S. Abduhari; Tadzher C. Shaik; Alsimar B. Adidul; Jimrashier H. Ladja; Ersin S. Saliddin; Akshay J. Adin; Fradzkhan A. Rumbahali; Alnadzri B. Sali; Jumadam M. Jemser; Shernahar K. Tahil
Natural Sciences Engineering and Technology Journal Vol. 5 No. 1 (2025): Natural Sciences Engineering and Technology Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/nasetjournal.v5i1.62

Abstract

In today's digital landscape, the protection of sensitive data from unauthorized access is a critical concern for organizations of all sizes. Robust access control mechanisms are essential for maintaining data security and preventing breaches. This study conducted a comparative analysis of three widely used access control methods: Role-Based Access Control (RBAC), Multi-Factor Authentication (MFA), and Strong Passwords. The research employed a mixed-methods approach, combining a quantitative analysis of simulated data with a qualitative review of recent literature. The Access Control Simulation Environment (ACSE) was developed to generate data on the effectiveness of each access control method in preventing unauthorized access attempts. The qualitative component involved a systematic review of Scopus-indexed publications from 2018 to 2024, focusing on the strengths, weaknesses, and best practices associated with each method. The simulation data revealed that MFA provided the highest level of protection against unauthorized access, followed by RBAC and then Strong Passwords. The qualitative analysis identified key strengths and weaknesses of each method, highlighting the importance of contextual factors in selecting the most appropriate access control mechanism. In conclusion, the findings underscore the need for a layered approach to access control, combining multiple methods to achieve optimal security. While MFA offers the strongest protection, RBAC and Strong Passwords remain crucial components of a comprehensive security strategy. The study provides practical recommendations for organizations seeking to implement and optimize access control mechanisms to mitigate the risk of unauthorized data access.
Enhancing Phishing Detection in Sulu, Philippines: A Machine Learning Approach to Combat Evolving Cyber Threats Benladin J. Warki; Aldam S. Ayyub; Ar-gifari A. Abdul Muktar; Sahier S. Ibrahim; Yusop S. Arbani; Ronnie E. Omar; Jurmilyn L. Muid; Jenelyn M. Mansul; Narsisa R. Ghamrasil; Nirfaisa E. Abduharim; Shernahar K. Tahil
Natural Sciences Engineering and Technology Journal Vol. 5 No. 1 (2025): Natural Sciences Engineering and Technology Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/nasetjournal.v5i1.65

Abstract

Phishing attacks are a growing threat to individuals and organizations worldwide, and Sulu, Philippines, is no exception. These attacks use deceptive emails, websites, and text messages to trick victims into revealing sensitive information such as login credentials, financial data, and personal details. Machine learning (ML) techniques have emerged as a promising solution for enhancing phishing detection due to their ability to learn patterns and adapt to new threats. This study investigates the effectiveness of ML approaches in enhancing phishing detection in Sulu, Philippines. A comprehensive dataset of phishing and legitimate websites was collected, incorporating features relevant to Sulu's context, such as local e-commerce platforms, government services, and banking institutions. Various ML algorithms, including Random Forest, Support Vector Machine, and Naive Bayes, were trained and evaluated on this dataset. The ML models demonstrated high accuracy in detecting phishing websites. The Random Forest model achieved the highest accuracy of 98.7%, followed by the Support Vector Machine with 96.5% accuracy and the Naive Bayes with 94.2% accuracy. Feature importance analysis revealed that specific features, such as URL structure, domain age, and the presence of login forms, played a crucial role in accurate classification. In conclusion, the findings suggest that ML techniques can significantly enhance phishing detection capabilities in Sulu, Philippines. Implementing these techniques in security solutions can help protect individuals and organizations from falling victim to phishing attacks.
Co-Authors Akshay J. Adin Aldam S. Ayyub Alnadzri B. Sali Alsimar B. Adidul Ar-gifari A. Abdul Muktar Benladin J. Warki Edrian S. Abduhari Ersin S. Saliddin Fradzkhan A. Rumbahali Jenelyn M. Mansul Jimrashier H. Ladja Jumadam M. Jemser Jurmilyn L. Muid Narsisa R. Ghamrasil Nirfaisa E. Abduharim Ronnie E. Omar Sahier S. Ibrahim Tadzher C. Shaik Yusop S. Arbani