Web applications are frequently targeted by cross-site scripting (XSS) attacks, a form of security breach where malicious scripts are injected to manipulate user interactions [1]. Identifying and mitigating these attacks are paramount in safeguarding information security. This study advances XSS detection through a Multi-Layer Perceptron (MLP) model, which has been trained and tested using a pre-segregated comprehensive dataset specifically designed to discern benign from malicious JavaScript. The research stands out for its high accuracy levels and a methodical approach to feature selection, crucial for enhancing model performance

Хажжуз А. (науч. рук. Авксентьева Е.Ю.) Advancing XSS Detection in Web Environments via MLP Modeling // Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, [2024]. URL: https://kmu.itmo.ru/digests/article/12005