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DDoS detection and mitigation using machine learning
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Gawande, Arpit Ramesh. DDoS detection and mitigation using machine learning. Retrieved from https://doi.org/doi:10.7282/T3HX1H13

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TitleDDoS detection and mitigation using machine learning
NameGawande, Arpit Ramesh (author); Birget, Jean-Camille (chair); Shende, Sunil (co-chair); Ramaswami, Suneeta (co-chair); Rutgers University; Camden Graduate School
Date Created2018
Other Date2018-05 (degree)
SubjectComputer Science, Machine learning, Denial of service attacks
Extent1 online resource (iii, 35 p. : ill.)
DescriptionDistributed Denial of Service (DDoS) attacks are very common nowadays. It is evident that the current industry solutions, such as completely relying on the In- ternet Service Provider (ISP) or setting up a DDoS defense infrastructure, are not sufficient in detecting and mitigating DDoS attacks, hence consistent research is needed. In this thesis we first tried to understand how DDoS attacks happen, then we discussed a way to detect DDoS attacks using machine learning tools at the routers, instead of setting up a centralized analysis system. We have proposed a standard communication architecture which can be used across all the networking devices for mitigating DDoS attacks. We have also created a simulation program to demonstrate our detection technique.
NoteM.S.
NoteIncludes bibliographical references
Noteby Arpit Ramesh Gawande
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T3HX1H13
Languageeng
CollectionCamden Graduate School Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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