TY  - EJOU
AU  - Alsulami, Abdulaziz A. 
AU  - Al-Haija, Qasem Abu 
AU  - Alturki, Badraddin 
AU  - Yafoz, Ayman 
AU  - Alqahtani, Ali 
AU  - Alsini, Raed 
AU  - Binyamin, Sami Saeed 

TI  - Efficient Malicious QR Code Detection System Using an Advanced Deep Learning Approach
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Cybersecurity; quick response (QR) code; deep learning; recurrent neural network (RNN); gated recurrent unit (GRU); long short-term memory (LSTM)

DO  - 10.32604/cmes.2025.070745