DocumentCode :
2125062
Title :
Restrain the Linkage to Malicious Web Pages though Negative Link Weight
Author :
Luo, Jiangfeng ; Zhu, Cheng ; Zhang, Weiming ; Liu, Zhong ; Huang, JinCai
fYear :
2008
fDate :
21-22 Dec. 2008
Firstpage :
262
Lastpage :
267
Abstract :
Currently, the search engine is mainly based on the Web content-identifying technique to deal with malicious Web pages. As long as the malicious content is identified, it is common to simply filter out the malicious pages or give some security warnings. They donpsilat distinguish the linkage to malicious pages from others during the pagepsilas rank. This paper mainly researches on the impact of the malicious Web pages on userpsilas surfing action and present a new surfing action model. Under the new surfing model, we put forward a new page rank algorithm with negative link weight penalty to restrain the linkage to malicious pages, in which the Web pages which link to malicious pages are punished. Subsidiary nodes are introduced to ensure the correctness and effectiveness of the algorithm under different conditions. Both theoretic analysis and simulation result show authority values of the pages linking to malicious pages will be reduced. It effectively restrains the linkage to malicious Web pages from the perspective of link analysis.
Keywords :
Internet; information retrieval; search engines; security of data; Web content-identifying technique; malicious Web pages; negative link weight penalty; page rank algorithm; search engine; surfing action model; Algorithm design and analysis; Analytical models; Couplings; Filters; Information retrieval; Joining processes; Knowledge acquisition; National security; Search engines; Web pages; Markov process; malicious webpage; negative link weight penalty; subsidiary node;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3488-6
Type :
conf
DOI :
10.1109/KAM.2008.56
Filename :
4732826
Link To Document :
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