Biblio

Online Social Networks (OSNs) are continuously suffering from the negative impact of Cross-Site Scripting (XSS) vulnerabilities. This paper describes a novel framework for mitigating XSS attack on OSN-based platforms. It is completely based on the request authentication and view isolation approach. It detects XSS attack through validating string value extracted from the vulnerable checkpoint present in the web page by implementing string examination algorithm with the help of XSS attack vector repository. Any similarity (i.e. string is not validated) indicates the presence of malicious code injected by the attacker and finally it removes the script code to mitigate XSS attack. To assess the defending ability of our designed model, we have tested it on OSN-based web application i.e. Humhub. The experimental results revealed that our model discovers the XSS attack vectors with low false negatives and false positive rate tolerable performance overhead.