<![CDATA[Cross-Site Scripting (XSS) attack is the most dominant attack in web applications. Its empowering attackers to inject malicious scripts into trusted web environments. The research paper proposed a machine learning-based framework for the detection and classification of XSS attacks. We use the dataset from Kaggle that contains textual web request patterns labeled as normal and malicious instances. Experimental analysis was performed to evaluate the effectiveness of multiple supervised learning algorithms including Logistic Regression, Random Forest, and Support Vector Machine. The methodology involved preprocessing, feature extraction, model training, testing, and performance evaluation parameters for binary classification.]]>
Copyrights © 2026
