Novel Detection Technique For Framejacking Vulnerabilities In Web Applications

Web applications are providing the front end to the web users and service providers to easily facilitate the on demand access of web services through IP. So, web is repeatedly attracting the attackers to play with majority of web users from the remote end by exploiting its identity. Day by day attackers are exploiting the new web vulnerabilities at any stage of web environment including client side, server side or communication side. From the literature it has been identified that it is required to identify the newly emerging attack vectors and also require an easily updatable detection framework. So, in this paper firstly variants of frame jacking vulnerabilities and its severity have been explored. Secondly, a framework to identify the variants of frame jacking vulnerabilities is proposed. Thereafter, the proposed framework has been analyzed on different attack vectors generated and identified from the standard open source vulnerable projects. The log files generated at various stages of these vulnerable projects are scrutinized to test the accuracy of the developed framework as live dataset. It benefits to train proposed system for newly emerging attack vectors. Further, to perform the depth study, same framework has also been analyzed on existing available dataset. It fits the framework accurately on existing standards. It is observed from the validation of framework that the result of LogitBoost is more accurate on both the datasets rather than the other classification techniques including Naïve Bayes and J48.