Machine Learning-Assisted Electronic Voting with SHA3-Based Security

Abstract

This paper aims to develop a software-based electronic voting system using a camera and computer as hardware. In this context, the paper addresses the electronic voting security issues by proposing a sophisticated system that can gather information from users, encrypt it using the globally recognized hash algorithm known as SHA-3, and validate it through facial recognition. This system is resilient against jamming, hacking, spoofing, data theft, insider threats, malware injection, physical tampering, side-channel attacks, network exploits, firmware manipulation, data extraction via memory dumps, supply chain attacks, lack of encryption, and exploitation of software vulnerabilities. Addressing these concerns is essential for future progress in secure, scalable, and reliable electronic voting platforms. The test results and findings of the proposed system underscore the need for ongoing research to enhance security, privacy, and efficiency in electronic voting technologies. We didn't use any large photo data set and hence we could avoid the cost of space and data training time. Therefore, we didn't need to compute the values of precision and recall values. Instead, we used cv2.CascadeClassifier('data/haarcascade_frontalface_default.x ml') for the face detection.