DocumentCode
2002788
Title
Link Farm Spam Detection Based on its Properties
Author
Wang, Yong ; Qin, Zhiguang ; Tong, Bin ; Jin, Jing
Author_Institution
Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol., Chengdu, China
Volume
2
fYear
2008
fDate
13-17 Dec. 2008
Firstpage
477
Lastpage
480
Abstract
Networks exist anywhere around us, say, the World Wide Web is a scale-free network whose vertices are Web pages and files, and edges are hyperlinks between Web pages and files. Employing the link architecture of World Wide Web network, search engines like Google help people locate resources efficiently. However, the performance of search engine is greatly decreased as search engine spam is involved. To handle the search engine spam problems, especially link farm spam, utilizing the degree distribution and average pathlength properties of Web network is one of the most novel breakthroughs in that normal Website is a scalefree network and the values of its properties are obviously different from those of properties of exceptional spam Website which is an instance of slightly-fully connected network. Through our thorough experiments, we find that these exceptional Websites are highly made up of spam pages, and our property-based approach has obvious efficacies on linkfarm detection, and in turn, enables search engines to provide more relevant results for users.
Keywords
Internet; Web sites; search engines; security of data; unsolicited e-mail; Google; Website; World Wide Web; link farm spam detection; scale-free network; search engine spam; Computational intelligence; Computer science; Computer security; Nearest neighbor searches; Search engines; Service oriented architecture; Unsolicited electronic mail; Web pages; Web sites; average length path; degree distribution; link farm spam; search engine spam;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Security, 2008. CIS '08. International Conference on
Conference_Location
Suzhou
Print_ISBN
978-0-7695-3508-1
Type
conf
DOI
10.1109/CIS.2008.156
Filename
4724822
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