Cross-site scripting (XSS) is a prevalent form of attack frequently identified in web applications. XSS enables malicious actors to swiftly inject malicious code either the web browser or web page. Malicious code might be employed by threat actors to illicitly obtain sensitive data, such as cookies, session tokens that are and other pertinent information, from unsuspecting users accessing web applications. In addition, they possess the ability to modify the material of the website. The aim of this study is to identify instances of Cross Site Scripting (XSS) Attacks. The process of detecting a cross-site scripting (XSS) attack involves several steps, including the creation of a sample website, conducting assessment using the XSSer tool, and categorizing the obtained outcomes as a dataset. The decision-making step involves the utilization of association filter-based features, while the detection phase employs machine learning methods such as XGBoost, Decision Tree, K-Nearest Neighbor (KNN), Naive Bayes, and AdaBoost Classifier. This work was created using Python-Jupyter Notebook and the XSSer tool. When analyzing the Adaboost Classification to the other four methods, it is observed that the Adaboost Classifier exhibits the highest level of precision in detecting.