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dc.contributor.authorPande, Rishikesh A.en_US
dc.date.accessioned2011-08-06T16:01:35Z
dc.date.available2011-08-06T16:01:35Z
dc.date.issued2004-05-11en_US
dc.identifier.otheretd-05182004-085925en_US
dc.identifier.urihttp://hdl.handle.net/10919/9943
dc.description.abstractNetwork worms that scan random computers have caused billions of dollars in damage to enterprises across the Internet. Earlier research has concentrated on using epidemiological models to predict the number of computers a worm will infect and how long it takes to do so. In this research, one possible approach is outlined for predicting the spatial flow of a worm within the local area network (LAN). The approach in this research is based on the application of mathematical models and variables inherent in plant epidemiology. In particular, spatial autocorrelation has been identified as a candidate variable that helps predict the spread of a worm over a LAN. This research describes the application of spatial autocorrelation to the geography and topology of the LAN and describes the methods used to determine spatial autocorrelation. Also discussed is the data collection process and methods used to extract pertinent information. Data collection and analyses are applied to the spread of three historical network worms on the Virginia Tech campus and the results are described. Spatial autocorrelation exists in the spread of network worms across the Virginia Tech campus when the geographic aspect is considered. If a new network worm were to start spreading across Virginia Tech's campus, spatial autocorrelation would facilitate tracking the geographical locations of the spread. In addition if an infection with a known value of spatial autocorrelation is detected, the characteristics of the worm can be identified without a complete analysis.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartetd-last.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectspatial spreaden_US
dc.subjectplant epidemiologyen_US
dc.subjectcomputer virusesen_US
dc.subjectnetwork wormsen_US
dc.titleUsing Plant Epidemiological Methods to Track Computer Network Wormsen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairArthur, James D.en_US
dc.contributor.committeememberMurali, T. M.en_US
dc.contributor.committeememberMarchany, Randolph C.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05182004-085925en_US
dc.date.sdate2004-05-18en_US
dc.date.rdate2004-05-28
dc.date.adate2004-05-28en_US


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