Open Access

ARTICLE

Efficient Malicious QR Code Detection System Using an Advanced Deep Learning Approach

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Badraddin Alturki³, Ayman Yafoz¹, Ali Alqahtani⁴, Raed Alsini¹, Sami Saeed Binyamin⁵

1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Cybersecurity, Faculty of Computer & Information Technology, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, Jordan
3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
5 Department of Computer and Information Technology, The Applied College, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Qasem Abu Al-Haija. Email:

(This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)

Computer Modeling in Engineering & Sciences 2025, 145(1), 1117-1140. https://doi.org/10.32604/cmes.2025.070745

Received 23 July 2025; Accepted 28 September 2025; Issue published 30 October 2025

Abstract

QR codes are widely used in applications such as information sharing, advertising, and digital payments. However, their growing adoption has made them attractive targets for malicious activities, including malware distribution and phishing attacks. Traditional detection approaches rely on URL analysis or image-based feature extraction, which may introduce significant computational overhead and limit real-time applicability, and their performance often depends on the quality of extracted features. Previous studies in malicious detection do not fully focus on QR code security when combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs). This research proposes a deep learning model that integrates AlexNet for feature extraction, principal component analysis (PCA) for dimensionality reduction, and RNNs to detect malicious activity in QR code images. The proposed model achieves both efficiency and accuracy by transforming image data into a compact one-dimensional sequence. Experimental results, including five-fold cross-validation, demonstrate that the model using gated recurrent units (GRU) achieved an accuracy of 99.81% on the first dataset and 99.59% in the second dataset with a computation time of only 7.433 ms per sample. A real-time prototype was also developed to demonstrate deployment feasibility. These results highlight the potential of the proposed approach for practical, real-time QR code threat detection.

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Keywords

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