AI-Driven Deep Structured Learning for Cross-Site Scripting Attacks

A substantial danger of attacks from numerous sources, with varying intensity and sophistication, is posed by the worldwide nature of online applications. A code injection attack known as cross-site scripting (XSS) occurs on the client side or via web browsers. Cross-Site Scripting (XSS), a severe attack that may be used to change a user's or an organization's data, is typical in online applications. This paper suggests a hybrid convolutional neural network (CNN) and machine learning (ML) framework for quickly and effectively identifying the XSS attacks to enhance the security. According to the simulation findings, the proposed model is more effective than the existing detection techniques. It generates results for XSS attack categorization that are more than 99.9% correct and is exceptionally resistant to XSS attack.