Design and Implementation for Malicious Links Detection System Based On Security Relevance of Webpage Script Text

Автор: Xing Rong, Li Jun, Jing Tao

Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis

Статья в выпуске: 3 vol.3, 2011 года.

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With the development of web technology, spreading of Trojan and viruses via website vulnerabilities is becoming increasingly common. To solve this problem, we propose a system for malicious links detection based on security relevance of webpage script text and present the design and implementation of this system. Firstly, according to the current analysis of malicious links, we describe requirements and the general design for detection system. Secondly we describe the security-related algorithm with mathematical language, and give the data structure of this algorithm. Finally, we analyze and summarize the experimental results, and verify the reliability and rationality of system.

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Text analysis, security relevance, malicious links

Короткий адрес: https://sciup.org/15011021

IDR: 15011021

Список литературы Design and Implementation for Malicious Links Detection System Based On Security Relevance of Webpage Script Text

  • ZHUGE Jianwei, YE Zhiyuan, ZOU Wei. “Research on Classification of Attack Technologies” [J]. Computer Engineering,Vol.31, No.21, pp.121-126, 2005.
  • LUO Chuan,XIN Mingting,LING Zhixiang. Analysis and realization of the web Trojan horse [J]. NETWORK & COMPUTER SECURITY,Vol.12, pp.83-85, 2007.
  • ZHUGE Jianwei , HAN Xinhui , ZHOU Yonglin. HoneyBow: an automated malware collection tool based on the high-interaction honeypot principle [J]. Journal on Communications,2007,28(12):8-13.
  • E. Glover, K. Tsioutsiouliklis, S. Lawrence, D. Pennock, and G. Flake. Using web structure for classifying and describing web pages. In Proc. of WWW, Hawaii, USA, May 2002. ACM Press.
  • WU Runpu, FANG Yong, WU Shaohua. Web Trojan Detection Model Based on Statistics and Code Characteristics Analysis [J]. Information and Electronic Engineering,Vol.1, pp.71-75, 2009.
  • A. Sun and E.-P. Lim. Web unit mining – finding and classifying subgraphs of web pages. In Proc. of 12th ACM CIKM, pages 108–115, New Orleans, LA, USA, Nov. 2003.
  • Han J, Kamber M. Data Mining: Concepts and Techniques SanMateo, CA: Morgan Kaufmann, 2000.
  • HAN Jia-Wei, MENG Xiao-Feng, WANG Jing, and LI Sheng-En. Research On Web Mining: A Survey [J]. JOURNAL OF COMPUTER RESEARCH & DEVELOPMENT, Vol. 38, No. 4, pp. 405-414, 2001.
  • XUE Wei-min and LU Yu-chang. Research On Text Data Mining [J]. Journal of Beijing Union University(Natural Sciences), Vol. 19, No. 4, pp. 59-63, 2005.
  • WU Runpu, FANG Yong, WU Shaohua. Web Trojan Detection Model Based on Statistics and Code Characteristics Analysis [J]. Information and Electronic Engineering,Vol.1, pp.71-75, 2009.
  • LEVINE J, GRIZZARD J, OWEN H. Application of a methodology to characterize rootkits retrieved from honeynets[A]. Proceedings of the Fifth Annual Information Assurance Workshop[C]. West Point, NY, USA, 2004. 15-21.
  • PROVOS N. A virtual honeypot framework [A]. Proceedings of 13th USENIX Security Symposium[C]. San Diego, CA, USA, 2004. 1-14.
  • Ali Ikinci, Thorsten Holz, Felix Freiling. Monkey-Spider : Detecting Malicious Websites with Low-Interaction HoneyClients[C]//Gesellschaft für Informatik. Proceedings of Sicherheit. Mannheim : University Mannheim,2008:233-244.
  • CHEN Ling, WANG Yi-jun and XUE Zhi. Detection of Web-site with Trojan Based on HoneyClient [J]. CHINA INFORMATION SECURITY, Vol. 5, pp. 75-77, 2010.
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