• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Wang, D. (Wang, D..) | Liu, Y. (Liu, Y..) | Zhao, W. (Zhao, W..) | Fu, L. (Fu, L..) | Du, X. (Du, X..)

收录:

Scopus PKU CSCD

摘要:

To deal with the problems, such as low coverage of found vulnerability injection points in complex web page, lacking of dynamical analysis for response message from target website faced by the detection system of XSS vulnerability, a method to detect XSS vulnerability based on user's behavior simulation is proposed to make improvement for the detection system of XSS vulnerability on extracting injection points, generating attack test vector and analyzing response results. By searching for a variety of the unformatted injection points through analyzing web page structure as well as taking into consideration the length of the string and the type of the character, the attack test vector is optimized and it can bypass the server filter function and shorten the vulnerability detection time. Test results show that the proposed method can improve the detection coverage rate of the injection point and the detection effect of XSS vulnerability. © 2017, Editorial Office of Journal of Dalian University of Technology. All right reserved.

关键词:

Detection; Ghost.py; Headless browser; XSS vulnerability

作者机构:

  • [ 1 ] [Wang, D.]College of Computer Science, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Liu, Y.]College of Computer Science, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Zhao, W.]College of Computer Science, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Fu, L.]College of Computer Science, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Du, X.]College of Computer Science, Beijing University of Technology, Beijing, 100124, China

通讯作者信息:

  • [Wang, D.]College of Computer Science, Beijing University of TechnologyChina

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

Journal of Dalian University of Technology

ISSN: 1000-8608

年份: 2017

期: 3

卷: 57

页码: 302-307

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

在线人数/总访问数:87/4299962
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司