MALICIOUS WEB BROWSER EXTENSION DETECTOR

Abstract

Malicious browser extensions raised a global threat towards web users, their tremendous spreading made internauts vulnerable to all sort of attacks that could be performed those extensions. multiple approaches and techniques were used by security experts to prevent and detect those ill extensions. In this report we propose a hybridization approach of static and dynamic techniques, geared with a machine learning model. The approach focuses on retrieving relevant malicious features, matching malicious pattern and defining new ones through examining the extensions behaviors in real-time on a legit environment with multi factors that work as a trigger to witness the various behaviors possible performed by the extension on the spot light. For our training model, we examined some of the top chrome store extensions, group of malicious extensions discovered by experts but mainly extensions that weren’t studied previously, which were detected by us later. The validation test reached 100% accuracy on several classifiers.