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dc.contributor.advisorRowe, Neil
dc.contributor.authorMcKenna, Sean F.
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractWeb robots are automated programs that systematically browse the Web, collecting information. Although Web robots are valuable tools for indexing content on the Web, they can also be malicious through phishing, spamming, or performing targeted attacks. In this thesis, we study an approach to Web-robot detection that uses honeypots in the form of hidden resources on Web pages. Our detection model is based upon the observation that malicious Web robots do not consider a resource’s visibility when gathering information. We performed a test on an academic website and analyzed the honeypots’ performance using Web logs from the site’s server. Our results did detect Web robots, but did not adequately detect the more sophisticated robots, such as those using deep-crawling algorithms with query generation.en_US
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsCopyright is reserved by the copyright owner.en_US
dc.titleDetection and classification of Web robots with honeypotsen_US
dc.contributor.secondreaderRohrer, Justin P.
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Scienceen_US
dc.subject.authorWorld Wide Weben_US
dc.description.serviceCivilian, Scholarship for Serviceen_US
etd.thesisdegree.nameMaster of Science in Computer Scienceen_US
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US

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