Publication Type:

Journal Article


Advances in Intelligent Systems and Computing, Springer Verlag, Volume 394, p.591-598 (2016)





Artificial intelligence, Attack prevention, Cross site scripting, Document object model, Java scripts, Learning algorithms, Learning systems, Plug-ins, Prevention techniques, Scoring systems, Security mechanism


The attacks on the users by exploiting the vulnerabilities of the browsers have increased at an alarming rate. The existing attack prevention strategies have failed miserably in most of the situations. Moreover, users have also not taken much care of configuring their browsers securely, using available extensions and plug-ins. This proposal puts forward an advanced XSS prevention technique by introducing a new scoring system for privilege levels and vulnerability levels of the contents rendered in the browser. The java scripts rendered in the browsers are stored, classified, and analyzed using machine learning algorithms. Machine learning can also be used to predict the browser quirks and generate an attacker pattern. The security mechanisms are also implemented inside the Document Object Model (DOM) to check the execution of dynamic scripts. © Springer India 2016.


cited By 0; Conference of International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, ICAIECES 2015 ; Conference Date: 22 April 2015 Through 23 April 2015; Conference Code:164469

Cite this Research Publication

N. S. Sunder and Gireesh, K. T., “Privilege-based scoring system against cross-site scripting using machine learning”, Advances in Intelligent Systems and Computing, vol. 394, pp. 591-598, 2016.