Authors : Ahmad Y. Alnajjar, Mohammed Anbar, Selvakumar Manickam, Omar Elejla and Homam El-Taj
Abstract: Due to their advent and widespread demand, the smart mobiles have increased the use of Quick Response (QR) code technology. However, some phishers have started using certain features of the QR code to spread phishing frauds through smartphones. The QR code is a matrix barcode that allows easy interaction between mobile devices and websites or printed material by removing the burden of manually typing a URL or contact information. Phishers have started using the QR code for the phishing attacks. In this study we have proposed a new approach called �QRphish� which detects URL phishing on QR code. It uses the QR code-specific features and the URL features to detect whether the QR code content has a URL phishing or not. The QR code specific features used in this research use the QR code content and its characteristics like length, type and level of error correction. This method uses the machine learning classification technique. The proposed approach is evaluated by using benchmark dataset; the result shows a high accuracy in term of detecting URL phishing.
Ahmad Y. Alnajjar, Mohammed Anbar, Selvakumar Manickam, Omar Elejla and Homam El-Taj, 2016. QRphish: An Automated QR Code Phishing Detection Approach. Journal of Engineering and Applied Sciences, 11: 553-560.