Detection of QR Code-based Cyberattacks using a Lightweight Deep Learning Model

Traditional intrusion detection systems rely on known patterns and irregularities. This study proposes an approach to reinforce security measures on QR codes used for marketing and identification. The former investigates the use of a lightweight Deep Learning (DL) model to detect cyberattacks embedded in QR codes. A model that classifies QR codes into three categories: normal, phishing, and malware, is proposed. The model achieves high precision and F1 scores for normal and phishing codes (Class 0 and 1), indicating accurate identification. However, the model's recall for malware (Class 2) is lower, suggesting potential missed detections in this category. This stresses the need for further exploration of techniques to improve the detection of malware QR codes. Despite the particular limitation, the overall accuracy of the model remains impressive at 99%, demonstrating its effectiveness in distinguishing normal and phishing codes from potentially malicious ones.