Online Election System to Avoid Fraud Voting by Using Cybersecurity Techniques with the Help of ML Techniques
DOI:
https://doi.org/10.15680/IJCTECE.2026.0902010Keywords:
Fraud Detection, Cybersecurity, Machine Learning (ML), Biometric Authentication, Anomaly Detection, End-to-End Encryption, Data Integrity, Identity Verification, Secure Voting ProtocolAbstract
Online election systems are becoming increasingly popular because they are fast and convenient; however, they are magnets for fraud and hacking. Bad actors look for weak spots in authentication, APIs, and user interfaces to tamper with votes, steal identities, or just try to disrupt the entire process. Old-school security, such as fixed rule-based systems, cannot keep up with the new tricks that fraudsters use. This is where machine learning plays a role. With ML, role fraud can be detected as it occurs, new threats can be predicted, and defenses can be adapted in real time. This study explores how machine learning can make online elections safer and more transparent in the future. We examine how these systems combat identity theft, prevent duplicate voting, and withstand major cyberattacks in the following sections. We propose to build an online election platform that combines tough cybersecurity protocols with smart ML models to keep fraud out and trust in.
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