Journal of Information Security Research ›› 2020, Vol. 6 ›› Issue (9): 0-0.


The Research of Discerning XSS Attack Based on FP-growth Optimized SVM Classifier


  • Received:2020-09-10 Online:2020-09-10 Published:2020-09-12



  1. 1. 江苏信息职业技术学院物联网工程学院
    2. 江苏信息职业技术学院
  • 通讯作者: 李仁杰

Abstract: Cross-site scripting (XSS) is a web-based security attack that is one of the most serious threats to Internet security today. Based on the principle of XSS attack detection based on Support Vector Machine (SVM) classifier, paper proposes An association detection algorithm (FP-growth) optimizes the XSS attacker detection method,It is verified by experiments that this method can effectively improve the accuracy of XSS detection compared with the common SVM detection method.

Key words: machine learning, XSS, SVM, FP-growth, attack, detection, prevention

摘要: 跨站脚本(XSS)攻击是一种基于Web的安全攻击,是目前影响互联网安全的最为严重的威胁之一.通过对支持向量机(SVM)分类器的XSS攻击检测相关技术的研究,提出了一种基于关联分析算法(FP-growth)优化SVM分类器的XSS攻击自动检测策略,实验结果证明该策略有效提高了XSS攻击检测精准度.

关键词: 机器学习, 跨站脚本, 支持向量机, FP-growth, 攻击, 检测方法, 防范