Machine Learning-Based CAPTCHA Defenses for Mobile and IoT Devices

Abstract

Machine learning-based CAPTCHA defenses have emerged as a promising approach to enhancing security on mobile and Internet of Things (IoT) devices. This research paper explores the application of machine learning techniques to develop robust CAPTCHA mechanisms tailored for deployment in resource-constrained environments. By leveraging advances in deep learning and pattern recognition, these defenses aim to thwart automated attacks while minimizing computational overhead. This paper examines the effectiveness of machine learning-based CAPTCHA defenses in mitigating threats specific to mobile and IoT ecosystems, such as bot attacks and unauthorized access attempts. Additionally, considerations regarding usability, privacy, and cross-platform compatibility are addressed to ensure practicality and user acceptance. Through empirical analysis and experimentation, this study provides insights into the efficacy and challenges of integrating machine learning-based CAPTCHA defenses into mobile and IoT environments, paving the way for enhanced security in an increasingly connected world.