A Grey Wolf Optimized XGboost-Multilayer Stacking Approach to Detect XSS Attacks

Abstract

Life is easier with web applications. However, data stealing has become a serious issue and can have unpleasant effects on human life. This is when attackers try to access sensitive users’ information or acquire illegal rights. Cross-site scripting is the most common attack that steals user information. It is also known as an XSS attack and appears on OWASP’s Top 10 Attacks as well. More than 70 percent of web-based applications are susceptible to XSS attacks. This paper presents a Grey Wolf optimizer based on the XGboost multilayer stacking approach for XSS attack detection. The grey wolf optimizer is also known as the GWO as feature optimization for machine learning. This is superior to FEP, PSO, and GSA algorithms as it overcomes real-world challenges. The proposed techniques categorize XSS and non-XSS with an accuracy of 99.5%, recall rate of 1, false positive rate of 0.004, precision of 98.5%, and F-degree of 99.28%. The proposed methodology also compares with several existing machine learning algorithms and ensemble classifier techniques. It shows that the proposed approach is more accurate than earlier.