Strengthening Mobile Banking Security With AI and Cryptographic Methods

Abstract

Mobile banking applications have revolutionized financial services by offering consumers quick and convenient access to their accounts. However, this convenience comes with significant security risks, including phishing attacks, data breaches, and unauthorized access. This study investigates these security challenges and explores mitigation strategies through the application of various algorithms. The focus is on two key areas: intrusion detection and data encryption. We assess the effectiveness of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) in detecting malicious activities. Our results indicate that CNNs outperform SVMs by achieving a 15% higher accuracy in identifying complex attacks, owing to their enhanced ability to interpret intricate patterns. In the domain of data encryption, we compare Elliptic Curve Cryptography (ECC) with the Advanced Encryption Standard (AES). While AES excels in terms of speed and robustness, ECC offers a more efficient solution with comparable security levels. Based on our findings, we recommend the adoption of hybrid encryption schemes and enhanced anomaly detection systems as potential mitigation strategies. The study underscores the importance of integrating multiple security approaches to protect mobile banking applications from evolving threats.