Year: 2024
Type: article
Abstract: In recent years, the increasing sophistication of cyberattacks has exposed significant vulnerabilities in traditional ATM security systems, primarily those relying on Personal Identification Number (PIN)-based authentication. This paper presents an advanced security framework that combines biometric facial recognition with a One-Time Password (OTP) system to create a dual-factor authentication mechanism aimed at fortifying ATM security. The integration of these technologies addresses the shortcomings of conventional PIN-based systems, offering a robust solution to prevent unauthorized access and reduce fraud. The proposed system utilizes machine learning algorithms for facial recognition, ensuring reliable and accurate user identification under various conditions. Complementing this is an OTP sent to the user's registered mobile device, which adds a secondary layer of verification. The results from the implementation show a significant decrease in unauthorized access attempts and fraudulent activities, thereby enhancing the overall security of ATM transactions without compromising on user convenience. Keywords: ATM security, facial recognition, OTP, biometric authentication, dual-factor authentication, fraud prevention
DOI: 10.55041/ijsrem37351
Authors: Trupti Shinde, Tanuja Nandre, Gaurav Wable
Institution: Vishwakarma University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4402273785