XSS: Cross-site Scripting Attack Detection by Machine Learning Classifiers
Prince Roy, Rajneesh Kumar, Pooja Rani, Tanmoy Saha Joy · 2022
Cross-site scripting (XSS) is a menacing attack predominately marked by owsap10. It is primarily caused by insufficient sanitization of the web application's input and endpoint. Typically, developers are unconcerned about this issue, which prompts the attacker to carry out this attack. To detect this attack, this paper will employ multiple machine-learning classifiers like Logistic Regression, AdaBoost, Naive Bayes, XGBoost, Decision Tree, and will conduct experiments on the Kaggle Cross-site scripting dataset. After conducting numerous experiments, we discovered that the AdaBoost classifier performs 0.03 percent better than previous work and achieves 99.92% accuracy.