Detect Malicious URL Based on the Multi Social Media Application

YEAR : 2022


Mobile specific web application differ significantly from their desktop counterparts in content, layout and functionality. Accordingly, existing techniques to detect malicious web applications are unlikely to work for such webapplications. In this paper, we design and implement kAYO, a mechanism that distinguishes between malicious and benign mobile web applications. kAYO makes this determination based on static features of a webpage ranging from the number of iframes to the presence of known fraudulent phone numbers. First, we experimentally demonstrate the need for mobile specific techniques and then identify a range of new static features that highly correlate with mobile malicious webapplications. We then apply kAYO to a dataset of over 350,000 known benign and malicious mobile web applications and demonstrate 90% accuracy in classification. Moreover, we discover, characterize and report a number of webpages missed by Google Safe Browsing and VirusTotal, but detected by kAYO.In doing so, we provide the first static analysis technique to detect malicious multiple mobile applications.



System : Intel i3 and above
Hard Disk : 40GB
RAM : Minimum 4GB
Processer : 64-bit, four-core, 2.5 GHz minimum per core


Front End Language : HTML, CSS, JAVA, JSP SERVELTS
Backend : My SQL
Operating System : Windows 10 or 11


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