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dc.contributor.advisorAhmed, Dr. Tarem
dc.contributor.authorIslam, Humayra
dc.date.accessioned2018-01-29T05:02:27Z
dc.date.available2018-01-29T05:02:27Z
dc.date.copyright2016
dc.date.issued2016-06
dc.identifier.otherID 11261008
dc.identifier.urihttp://hdl.handle.net/10361/9276
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Masters of Science in Electrical and Electronic Engineering, 2016.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (page 70-72).
dc.description.abstractThe huge amount of traffic in backbone IP networks produces various kinds of anomalies in data packets. Distinct classifiers have been developed to deal with this anomalous data. These classifiers typically have predefined number of classes and use supervised learning methods. Some classifiers apply windowing method to make the huge data scalable into small groups. In this work, a new method for the classification of anomalous data have been applied with unsupervised learning using Correspondence Analysis (CA). Correspondence Analysis does not need a predefined number of clusters to begin with, and can handle comparatively large amounts of data. Results have been compared with other clustering techniques, which are applied on real data from the US Abilene backbone network. The results indicate that the proposed method is promising in classifying anomalies on the basis of frequencies of anomalous facade.en_US
dc.description.statementofresponsibilityHumayra Islam
dc.format.extent72 pages
dc.language.isoenen_US
dc.publisherBRAC Univeristyen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectAnomalyen_US
dc.subjectClusteren_US
dc.subjectQR codeen_US
dc.subjectClustering algorithmen_US
dc.titleAnomaly clustering based on correspondence analysisen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Electrical and Electronic Engineering, BRAC University
dc.description.degreeM. Electrical and Electronic Engineering 


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