Using machine learning to detect qrljacking to prevent multichannel phishing on applications or iot devices

Abstract

Aspects of the disclosure relate to using multiple machine learning models and a sand-box environment to detect malicious uniform resource locators (URLs) and login pages stored in a QR code. An application on a computing device will augment QR code data stored in a QR code and the computing device sends the QR code data and computing device metadata to a deep learning computing platform. The QR code data may comprise a URL and communication protocol information. The deep learning computing platform will generate, by multiple machine learning models and a sand-box environment, a sand-box score using the URL, a user-based score using the computing device metadata, and a connections score using the communication protocol information. The deep learning platform then determines whether the computing device should reject and delete the QR code data based on whether the scores are below a pre-determined threshold.