Detection and mitigation of critical web vulnerabilities and attacks like
cross-site scripting (XSS), and cross-site request forgery (CSRF) have been a
great concern in the field of web security. Such web attacks are evolving and
becoming more challenging to detect. Several ideas from different perspectives
have been put forth that can be used to improve the performance of detecting
these web vulnerabilities and preventing the attacks from happening. Machine
learning techniques have lately been used by researchers to defend against XSS
and CSRF, and given the positive findings, it can be concluded that it is a
promising research direction. The objective of this paper is to briefly report
on the research works that have been published in this direction of applying
classical and advanced machine learning to identify and prevent XSS and CSRF.
The purpose of providing this survey is to address different machine learning
approaches that have been implemented, understand the key takeaway of every
research, discuss their positive impact and the downsides that persists, so
that it can help the researchers to determine the best direction to develop new
approaches for their own research and to encourage researchers to focus towards
the intersection between web security and machine learning.

本文提出一份调查报告，介绍机器学习应用于 Web 安全领域，以识别和防止 XSS 和 CSRF 攻击的研究进展，讨论了各种方法的优劣，以期帮助研究人员确定开发新方法的最佳方向并鼓励他们关注 Web 安全和机器学习的交集。