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dc.contributor.advisorKazi, Sadia Hamid
dc.contributor.authorNur, Md. Walid Bin
dc.date.accessioned2018-01-03T04:30:08Z
dc.date.available2018-01-03T04:30:08Z
dc.date.copyright2017
dc.date.issued2017
dc.identifier.otherID 13101112
dc.identifier.urihttp://hdl.handle.net/10361/8878
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 33-35).
dc.description.abstractIoT has the potential to affect our ways of life. It is the next step of Internet where all the physical objects around us will be connected to each other. According to Gartner, by 2020 there will be over 26 billion connected devices. However, the security of such a big network of interconnected devices is of paramount importance. According to a report from Russian-based Kaspersky Labs, botnets - not spam, viruses or worm- currently pose the biggest threat to the Internet.However, few works that have been done on this issue in the recentpast are not successful on themselves alone. In this paper, I present an in depth understanding of the problem and propose a mechanism to counter this issue. My proposal is based on Transductive Confidence Machines, which was previously proposed as a mechanism to provide confidence measures on classification decisions. It proposes to make use of this algorithm with the help of honeypot to collect attack data and uses these data to make the system more proficient.en_US
dc.description.statementofresponsibilityMd. Walid Bin Nur
dc.format.extent35 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectIoTen_US
dc.subjectDDoS attacken_US
dc.subjectHoneypoten_US
dc.subjectTCM-kNNen_US
dc.titleAnalysis on IOT Botnet and DDOS attacken_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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