Real-Time Mobile Malicious Web pages Detection Using kA YO

Abstract

The content, appearance, and practicality of mobile web sites are strikingly different from those of their
desktop counterparts. Consequently, current methods for detecting fraudulent websites are unlikely to
be able to adapt to these kinds of situations. kAYO, a mechanism for distinguishing between malicious
and benign mobile websites, is the framework we use to build and implement this system. KAYO's
guarantee is based on the quantity of iframes on a website, as well as the proximity of world-renowned
deceptive phone numbers. Before we can discover which current static elements are most closely
associated with malicious mobile web sites, we must first show the technical requirements for mobile-
specific approaches. A dataset of more than 350,000 harmful and benign mobile web pages is then used
to show 90% accuracy in kAYO classification. Google Safe Browsing didn't identify these sites, but kAYO
did, and we defined and reported them. A browser plugin for kAYO is also in the works to keep
consumers safe from fraudulent mobile websites. Thus, we offer a fundamental static analysis method
for detecting malicious mobile web pages.