Machine Learning Based XSS Attack Identification and Prevention in Cloud Environments

Cross-Site Scripting (XSS) attacks pose a significant security threat to modern web applications, particularly in cloud environments where resources and data are shared extensively among users and services. These attacks allow malicious actors to inject and execute harmful scripts in view of a legitimate user’s session, leading to unauthorized access, data breaches, and compromised system integrity. The dynamic and scalable nature of cloud platforms further exacerbates the hurdle detecting and mitigating such threats. This study presents a forward-thinking model based on machine learning (ML) algorithms to enhance the detection and prevention of cross-site scripting (XSS) attacks. By analyzing attributes such as script patterns, encoding techniques, and the presence of specific keywords, ML models can correctly classify legitimate and malicious scripts. The proposed framework includes data preprocessing, feature extraction, and the deployment of supervised learning methods, including decision trees and SVM, and neural networks, to ensure precise detection. Through continuous training and real-time monitoring, the ML- driven system adapts to new attack patterns, providing robust protection against emerging threats. Empirical evidence suggests that machine learning methodologies substantially improve detection accuracy and response times when contrasted with traditional signaturebased methods. The paper concludes by emphasizing the critical need for incorporating automated, intelligent security solutions to protect cloud environments from emerging XSS threats.