A web page malicious script detection system

Author

Siyue Zhang ; Weiguang Wang ; Zhao Chen ; Heng Gu ; Jianyi Liu ; Cong Wang

Author_Institution

Beijing Univ. of Posts & Telecommun., Beijing, China

fYear

2014

Firstpage

394

Lastpage

399

Abstract

Security risks brought by Web page information has been a matter that can no longer be ignored. Malicious script is a major challenge the Web sites security is facing currently. According to the data from the Google Research Centre, more than 10% of Web pages is malicious. Especially in China, the proportion of malicious Web pages has reached 43.21%. This paper presents a detection system which is used to locate the malicious scripts in Web pages. It acquires and builds up malicious code features base, URL of hidden links base etc. based on safety data published on security research Web sites. The Web crawler is applied to collecting Web pages source code in this system and learning algorithm for classification is used to train the classifier. The classification results would be evaluated and improved in the end.

Keywords

Web sites; invasive software; pattern classification; source code (software); China; Google Research Centre; URL; Web crawler; Web page information; Web page malicious script detection system; Web page source code collection; Web site security; classifier training; hidden-link base; learning algorithm; malicious code feature base; malicious script location; safety data; security risks; Classification algorithms; Feature extraction; HTML; Security; Training; Uniform resource locators; Web pages; Crawler; Hidden link; Malicious script; Script detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Cloud Computing and Intelligence Systems (CCIS), 2014 IEEE 3rd International Conference on

Print_ISBN

978-1-4799-4720-1

Type

conf

DOI

10.1109/CCIS.2014.7175767

Filename

7175767