Assessing the Impact of Adversarial Machine Learning Techniques on the Robustness and Reliability of Payment Authentication Systems

Abstract

Payment authentication systems play a critical role in ensuring the security and integrity of financial transactions in the digital era. With the increasing adoption of machine learning techniques in these systems, concerns have arisen regarding their vulnerability to adversarial attacks. Adversarial machine learning techniques, such as evasion attacks and poisoning attacks, can manipulate the input data or exploit vulnerabilities in the learning algorithms to deceive or compromise the authentication systems. This research aims to assess the impact of adversarial machine learning techniques on the robustness and reliability of payment authentication systems. By conducting a comprehensive analysis of various attack scenarios and evaluating the effectiveness of existing defense mechanisms, this study seeks to identify potential vulnerabilities and propose strategies to enhance the resilience of these systems against adversarial attacks. The findings of this research contribute to the development of more secure and trustworthy payment authentication systems, strengthening the overall security of the financial ecosystem in the face of evolving adversarial threats.