A Novel Approach to Detect XSS Attacks in Real Time Online Social Networking
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 exp
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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. Keywords Online social network
OSN XSS Intruder SQL injection
1 Introduction Today many latest technologies growing very fast. Online social networks (OSN) are one of the connecting technologies to serve for people working in web. Considerable big amount of secret data of social network users are shared in common without getting prior permission from original authors which carries K.G. Maheswari (&) Department of MCA, Institute of Road and Transport Technology, Anna University, Erode, Tamil Nadu, India e-mail: [email protected] R. Anita Department of EEE, Institute of Road and Transport Technology, Anna University, Erode, Tamil Nadu, India e-mail: [email protected] © Springer Science+Business Media Singapore 2017 P. Deiva Sundari et al. (eds.), Proceedings of 2nd International Conference on Intelligent Computing and Applications, Advances in Intelligent Systems and Computing 467, DOI 10.1007/978-981-10-1645-5_30
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personal data opinions, views, audio etc. Hence, these online social networks have personal related big data of single or group of people. Because of the large data usage, there is large amount of threads also available in web. Therefore, there is a heavy need for protecting the data in online networking to implement certain authenticate methods. This paper discusses web intrusion detection in OSN which involves SQL injection attack, XSS and cyber-bullying attack. The proposed method analyzes the social network features integrating with textual features improving the accuracy of automatic detection of cyber-bullying. The recent generation have many social connections through networking account and each day working towards it. The intruders easily corrupt the original data by taking correct information from your social networking profile [1] and posts, then plan their attacks based on the interests and likes of your profile. This personal data attack can be considered as “social engineering” and it creates individual data loss, more difficult to recognize. A user often has trusted “friends
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