Xss References

In this paper, S. A. Alaboudi, S. Al-Sudairy et al (2019) [12] reviewed the use of machine learning techniques for the detection and prevention of cross-site scripting attacks. They analyze various machine learning algorithms that have been used for this purpose, including decision trees, support vector machines, and neural networks. The authors also discuss the different types of XSS attacks and the challenges involved in detecting them. They conclude that machine learning techniques can be effective in detecting and preventing XSS attacks, but further research is needed to improve their accuracy and efficiency.

R. Kumar and P. Kumar et al (2019) [13] presented a systematic literature review of the use of machine learning techniques for detecting cross-site scripting attacks. The authors analyze the different types of machine learning algorithms that have been used for this purpose, including rule-based systems, decision trees, and support vector machines. They also discuss the datasets that have been used to train and test these algorithms. The authors conclude that machine learning techniques can be effective in detecting XSS attacks, but more research is needed to improve their accuracy and to develop real-time detection systems.

This paper provides a survey of the different machine learning techniques that have been used for detecting cross-site scripting attacks. The authors analyze the strengths and weaknesses of these techniques and compare their performance on various datasets. They also discuss the challenges involved in detecting XSS attacks and the potential solutions for these challenges. The authors conclude that machine learning techniques can be effective in detecting XSS attacks, but more research is needed to develop real-time detection systems that can handle the dynamic nature of web applications.

In this paper, the authors survey the different machine learning techniques that have been used for detecting cross-site scripting attacks. They analyze the strengths and weaknesses of these techniques and compare their performance on various datasets. The authors also discuss the different features that have been used to train these algorithms and the challenges involved in detecting XSS attacks. They conclude that machine learning techniques can be effective in detecting XSS attacks, but more research is needed to improve their accuracy and to develop real-time detection systems.

This paper presents a survey of the different machine learning techniques that have been used for detecting cross-site scripting attacks. The authors analyze the strengths and weaknesses of these techniques and compare their performance on various datasets. They also discuss the challenges involved in detecting XSS attacks and the potential solutions for these challenges. The authors conclude that machine learning techniques can be effective in detecting XSS attacks, but more research is needed to develop real-time detection systems that can handle the dynamic nature of web applications.