Journal on Communications ›› 2014, Vol. 35 ›› Issue (9): 32-39.doi: 10.3969/j.issn.1000-436x.2014.09.004

• PaperⅠ Network attack and Prevention • Previous Articles     Next Articles

Light-weight self-learning approach for URL classification

Hong-zhou SHA1,2,3,Zhou ZHOU2,3,Qing-yun LIU2,3,Peng QIN2,3   

  1. 1 Department of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China
    3 National Engineering Laboratory for Information Security Technology,Beijing 100093,China
  • Online:2014-09-25 Published:2017-06-14
  • Supported by:
    The National High Technology Research and Development Program of China(863 Program);The National Natural Science Foundation of China

Abstract:

A self-learning light-wight (SLW) is proposed.SLW is the first to introduce access relations and have the char-acteristics of feedback and self-learning.SLW approach starts from the seed set which includes known malicious pages.Then,it automatically figures out users with low credibility based on the seed set and the visit relation database.Finally,the access records of these users are used to identify other malicious pages.Experimental results indicate that SLW ap-proach can significantly improve the efficiency of malicious pages detection and reduce the average detection time com-pared with other conventional methods.

Key words: URL classification, blacklist, access relation, malicious Web page, Web page evaluation

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