Refinement of Voting System through Visual Cryptography and Multi-factor Authentication to Further Mitigate Clone Phishing Attack

Olamide Olanubi

School of Science, Engineering and Environment, University of Salford, 43 Crescent, Salford M5 4WT, United Kingdom.

Opeyemi Joshua Adelowo

Babcock University, Ilishan Remo, Ogun State, Nigeria.

Emmanuel Ifeanyi Obeagu *

Department of Medical Laboratory Science, Kampala International University, Uganda.

*Author to whom correspondence should be addressed.


Abstract

Background: The growing concern over phishing attacks on voting systems, particularly with the rise of online voting, has highlighted vulnerabilities in elections. The convenience of internet voting has led to security risks like clone phishing attacks. While online voting offers accessibility benefits, security, privacy, and usability issues have arisen. Multi-factor authentication (MFA) has been proposed to enhance security in mobile internet voting systems. Visual cryptography, dividing images into shares for decentralized data storage, is suggested to counter clone phishing. MFA's effectiveness in various sectors is established, and secure voting systems combining visual cryptography and blockchain have been proposed. This research sought to bolster the security of the voting system using visual cryptography and multi-factor authentication (MFA), with the goal of increasing voter trust and the reliability of the voting procedure, by designing a secure system and evaluating its performance, accuracy, and accessibility.

Methodology: The researchers developed an improved voting system using visual cryptography and multi-factor authentication, assessed its performance against Eligo and Voxvote, utilized Java for programming, MYSQL via XAMPP for the database, HTML, CSS, JavaScript, and PHP (Laravel) for client-server sides. The Scrum agile methodology was followed, employing brief sprints for adaptive development. Evaluation was done through web tools and benchmarks. The system's architecture and flowchart were presented, featuring an interactive GUI, Java 2 platform, MySQL, PHP 7, and specific hardware/software requirements. System setup included database and server configuration, fingerprint scanner installation, and Java runtime setup. Deployment involved executing a built Java program, and system testing was conducted on Windows 10, utilizing the Apache server and initiating the "Electronic Voting" program for online multi-factor authentication.

Results: Achieving a performance rate of 92%, the developed system surpassed its competitors Eligo and Voxvote, which achieved scores of 15% and 33% correspondingly, as indicated by the data. Eligo and Voxvote attained scores of 88% and 80% individually, whereas the newly designed system obtained a rating of 93% in terms of accessibility. Nonetheless, the research also highlighted specific downsides, encompassing intricacy, reluctance to embrace change, and technological barriers. To tackle these issues and enhance the adoption of such systems, these limitations underscore the necessity for further investigation and advancement.

Conclusion: The study demonstrates that integrating multi-factor authentication and visual cryptography significantly enhances voting system security, reducing clone phishing risks. Visual cryptography secures decryption keys, preserving voting integrity, while multi-factor authentication adds defence against unauthorized access. The researcher's online voting system outperforms competitors in performance metrics and accessibility. The study underscores the importance of combining these techniques for improved voting system reliability and security.

Keywords: Improvement of voting system, visual cryptography, multi-factor authentication, clone phishing attack


How to Cite

Olanubi , Olamide, Opeyemi Joshua Adelowo, and Emmanuel Ifeanyi Obeagu. 2023. “Refinement of Voting System through Visual Cryptography and Multi-Factor Authentication to Further Mitigate Clone Phishing Attack”. Asian Journal of Research in Computer Science 16 (4):145-60. https://doi.org/10.9734/ajrcos/2023/v16i4379.

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