Implementation of an intelligent antimalware system for the detection of malicious links in QR codes

Abstract

The increasing use of QR codes across various sectors has facilitated the transfer of information but has also exposed users to new cybersecurity threats, such as quishing, a variant of phishing that leverages these codes to redirect users to malicious websites. To address this issue, the study aimed to implement an antimalware system that employs machine learning alongside the VirusTotal API to analyze and classify links embedded in QR codes in real time. The methodology was structured into four stages: capturing and decoding QR codes using OpenCV, analyzing extracted URLs with the VirusTotal API, issuing preventive alerts based on the link classification, and evaluating system performance with a dataset of 100 QR codes (50 safe and 50 malicious). The results showed 100 % accuracy, 95 % sensitivity, and an average response time of 48.95 ms. No false positives were detected, and only a small number of false negatives were observed, although some codes were classified as uncertain due to insufficient information from VirusTotal. It is concluded that the system is a suitable and adaptable tool for preventing quishing attacks, with potential for implementation in mobile applications and payment systems, as well as possible expansions to other visual encoding technologies.