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dc.contributor.authorVangapandu, Krishna Bhargava
dc.date.accessioned2014-03-04T16:22:39Z
dc.date.available2014-03-04T16:22:39Z
dc.date.issued2008-12
dc.identifier.othervangapandu_krishna_b_200812_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/vangapandu_krishna_b_200812_ms
dc.identifier.urihttp://hdl.handle.net/10724/25341
dc.description.abstractThe use of URL redirections has been recently studied to filter spam as email and web spammers use redirection to camouflage their web pages. However, many web sites employ redirection for legitimate reasons such as logging, load-balancing and security. While a majority of the studies on URL redirection focused on spam redirection we provide a holistic view of the use of URL redirections in the Internet. We performed a redirection study on various sets of URLs that includes known legitimate and spam websites. We observed that URL redirections are widely used in today's Internet with more than 40% of URLs in the top websites redirecting for various reasons. It was observed that redirections occur in chains whose length on an average was close to 1.4. We also observed that server-side redirection is prominent in both legitimate and spam redirections. Differing from legitimate redirections, spam redirections employed JavaScript redirections more often. Furthermore, a very high percentage of spam redirections lead to an external domain. Apart from providing a quantitative view of URL redirections, we also provide a further classification of legitimate redirections based on the reason of redirection. We expect that our measurement results and classifications to provide a better understanding of the usage of URL redirection, which could help improve the spam filtering and other applications that rely on URLs as the web identifiers.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectURL
dc.subjectRedirections
dc.subjectJavaScript
dc.subjectSpam
dc.subjectWeb-Crawlers
dc.titleA measurement study of Web redirections in the Internet
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorKang Li
dc.description.committeeKang Li
dc.description.committeeShelby Funk
dc.description.committeeJohn A. Miller


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