Abstract: Cross site scripting (XSS) is one of the major threats to the web application security, where the research is still underway for an effective and useful way to analyse the source code of web application and removes this threat. XSS occurs by injecting the malicious scripts into web application and it can lead to significant violations at the site or for the user. Several solutions have been recommended for their detection. However, their results do not appear to be effective enough to resolve the issue. This paper recommended a methodology for the detection of XSS from the PHP web application using genetic algorithm (GA) and static analysis. The methodology enhances the earlier approaches of determining XSS vulnerability in the web application by eliminating the infeasible paths from the control flow graph (CFG). This aids in reducing the false positive rate in the outcomes. The results of the experiments indicated that our methodology is more effectual in detecting XSS vulnerability from the PHP web application compared to the earlier studies, in terms of the false positive rates and the concrete susceptible paths determined by GA Generator.

Abdalla Wasef Marashdih, Zarul Fitri Zaaba and Herman Khalid Omer, “Web Security: Detection of Cross Site Scripting in PHP Web Application using Genetic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 8(5), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080509

@article{Marashdih2017,
title = {Web Security: Detection of Cross Site Scripting in PHP Web Application using Genetic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080509},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080509},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {5},
author = {Abdalla Wasef Marashdih and Zarul Fitri Zaaba and Herman Khalid Omer}
}