Abdulaziz A. Alsulami1, Qasem Abu Al-Haija2,*, Badraddin Alturki3, Ayman Yafoz1, Ali Alqahtani4, Raed Alsini1, Sami Saeed Binyamin5
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1117-1140, 2025, DOI:10.32604/cmes.2025.070745
- 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… More >