Jan 1, 2021 · This paper aims to use machine learning to detect XSS attacks using various ML (machine learning) algorithms and to compare the performance of the algorithms in detecting XSS...

Mar 23, 2023 · We present a complete overview of recent machine learning and neural network-based XSS attack detection techniques in this paper, covering deep neural networks, decision trees, web-log-based detection models, and many more.

A Machine Learning Model to classify samples as XSS (Cross Site-Scrtpting) Resources

There are ample of methodologies being applied in the detection of XSS attacks using supervised learning, unsupervised learning, reinforcement learning, deep learning and metaheuristic algorithms. We present a survey of the recent approaches being applied by the numerous researchers in their proposed models.

In this paper, we focus on detecting XSS attacks using six different machine learning classifiers that classify web pages into two categories: XSS or non-XSS based on several extracted features.

This paper aims to use machine learning to detect XSS attacks using various ML (machine learning) algorithms and to compare the performance of the algorithms in detecting XSS attacks in web applications and websites.

Apr 14, 2025 · Hyperparameter tuning is performed using GridSearchCV, optimizing the model’s predictive capabilities. Experimental results demonstrate a 99.70% accuracy, with 100% recall and 99.36% precision, highlighting the model’s effectiveness in detecting XSS attacks while minimizing false alarms.

This paper investigates the use of machine learning to build classifiers to allow the detection of XSS. Establishing our approach, we target the detection modus operandi of XSS attack via two features: URLs and JavaScript.

Mar 5, 2025 · The paper presents a comprehensive study on detecting Cross-Site Scripting (XSS) attacks using machine learning and deep learning techniques. XSS attacks pose significant threats to web applications, compromising user data and system integrity.

Apr 28, 2025 · According to the Open Web Application Security Project (OWASP), Cross-Site Scripting (XSS) is a critical security vulnerability. Despite decades of research, XSS remains among the top 10 security vulnerabilities. Researchers have proposed various techniques to protect systems from XSS attacks, with machine learning (ML) being one of the most widely used methods. An ML model is trained on a ...