DocumentCode :
3599879
Title :
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
Link To Document :
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