Enhancing Web Security: Detecting and Preventing Reflected Cross-Site Scripting (XSS) Attacks

Suprabha Raju, Suraj (2024) Enhancing Web Security: Detecting and Preventing Reflected Cross-Site Scripting (XSS) Attacks. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (2MB) | Preview

Abstract

Cross-Site scripting attacks are a significant threat to web application security. This research focuses on the detection and prevention of reflected XSS attacks through a different approach which makes use of different methods. By implementing security methods such as input filtering, sanitization, output encoding, CSRF protection a framework is developed which is successful in preventing XSS attacks. The proposed model also includes live monitoring which allows for a quick response to detected threats. This study enhances web security by preventing and mitigating the risks posed by XSS attacks to web applications.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hafeez, Khadija UNSPECIFIED
Uncontrolled Keywords:	XSS attacks; web security; Cross-Site Scripting (XSS); CSRF protection; Artificial intelligence (AI)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	31 Jul 2025 11:36
Last Modified:	31 Jul 2025 11:36
URI:	https://norma.ncirl.ie/id/eprint/8378

Actions (login required)

View Item