CHAPTER ONE
INTRODUCTION
1.1 Background of Study
Cross-site scripting (XSS) is a type of computer security vulnerability typically found in web applications. XSS enables attackers to inject client-side scripts into web pages viewed by other users. A cross-site scripting vulnerability may be used by attackers to bypass access controls such as the same-origin policy. Cross-site scripting carried out on websites accounted for roughly 84% of all security vulnerabilities documented by Symantec as of 2007. Their effect may range from a petty nuisance to a significant security risk, depending on the sensitivity of the data handled by the vulnerable site and the nature of any security mitigation implemented by the site's owner.
1.2 Statement of the Problem
Web applications are increasingly becoming the primary medium for delivering services and information. However, this growth has been accompanied by a corresponding rise in security threats, with cross-site scripting being one of the most prevalent and dangerous attacks. Existing detection methods often rely on static analysis or signature-based approaches, which are insufficient for detecting novel or obfuscated XSS attacks. Moreover, these methods often produce high false positive rates, leading to alert fatigue and reduced effectiveness. Therefore, there is a need for a more robust and accurate detection system that can identify XSS attacks in real-time while minimizing false positives.
1.3 Aim and Objectives of the Study
The aim of this study is to develop a detection system for cross-site scripting attacks using dynamic analysis and fuzzy inference system. The specific objectives are to:
