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Published in:
2017 | OriginalPaper | Chapter
A Novel Approach to Detect XSS Attacks in Real Time Online Social Networking
Authors : K. G. Maheswari, R. Anita
Published in: Proceedings of 2nd International Conference on Intelligent Computing and Applications
Publisher: Springer Singapore
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Abstract
In a real time network scenario, online social networks (OSN) play a significant role in connecting and growing business and technology. This technology gathers much information and share secret data among network. This attitude gives the intruders to exploit the original information. This paper contributes for major widely spread and critical OSN vulnerability. XSS, popularly noted as a one-click attack or session riding attack which is the most common malicious attack that exploits the trust that a site has in a user’s browser. Proposed method is a XSS attack detection mechanism for the client side. It focuses on the matching of parameters and values present in a suspected request with a form’s input fields and values that are being displayed on a webpage. Next to address concerns of offensive content over Internet. The proposed method analyzes the social network features integrating with textual features improving the accuracy of automatic detection of XSS.