QR Shield: A Dual Machine Learning Approach Towards Securing QR Codes

Abstract

Quick Response (QR) codes, extensively employed due to their compatibility with smartphone technology and the technological advances of QR code scanners, have become a crucial aspect of modern life. With the ever-increasing adoption and utilization of QR codes in various real-life contexts, finding effective and efficient security mechanisms to maintain their integrity has become paramount. Despite their popularity, QR codes have been exploited as potential attack vectors through which attackers encode malicious URLs. Such attacks have become a critical concern, necessitating effective countermeasures to mitigate them. In response to this pressing issue, this research paper introduces QR Shield, a dual machine learning-based model designed to address the security vulnerabilities inherent in QR codes. Following extensive testing of multiple machine learning algorithms, two of the most promising algorithms have been integrated into this model, namely the Random Forest (RF) and XGBoost algorithms. The QR Shield employs these sophisticated machine learning algorithms to accurately identify and detect malicious URLs embedded within QR codes, utilizing a benchmark dataset of URLs. Through rigorous evaluation using four key metrics, the effectiveness of the QR Shield is demonstrated, with experimental results showcasing an impressive accuracy rate of 96.8%. Based on these outcomes, the QR Shield exhibits a high potential to detect malicious URLs embedded within QR codes, which confirms the ability to generalize the proposed QR Shield to various real-life domains and applications. Additionally, the present study contributes significantly to the broader field of QR code security by offering comprehensive insights into the efficacy of supervised machine learning models in enhancing QR code security and privacy, thus paving the way for future advancements in this critical area of research.