Enhancing security in QR code (Short URL) using NLP & CNN based framework (KEY IJP************639)

• Vishaka Vijay Kotian

Abstract

This research paper focuses on the identification of malicious QR codes, a growing concern in cybersecurity as their usage expands across various sectors. Malicious QR codes can lead to phishing attacks, data breaches, and malware installations, posing significant risks to users and organizations alike. The study presents a novel framework for detecting and analyzing malicious QR codes, integrating machine learning algorithms with traditional security measures. Through a comprehensive dataset of QR codes both benign and malicious, the research evaluates various features that distinguish harmful codes from legitimate ones. Results indicate that certain patterns, such as URL characteristics and code structure, can effectively predict malicious intent with high accuracy. Additionally, user awareness and education are assessed as vital components in mitigating risks. The paper concludes with practical recommendations for developing effective scanning tools and implementing security protocols, thereby enhancing protection against the threats posed by malicious QR codes in an increasingly digital environment.