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Vijay N. Gadepally. Do, Emily H. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2020-03-24T15:35:52Z 2020-03-24T15:35:52Z 2019 2019 https://hdl.handle.net/1721.1/124240 1145005007 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 61-65). Detecting and classifying attacks on computer networks is a significant challenge for network providers and users. This thesis project builds a deep neural network to detect and classify network attacks. Our approach based on the hypothesis that each type of network attacks generates a distinguishable change in the entropies of certain features of network flows. To generate a training and validation dataset, synthetic attacks of different types and levels of intensity are injected to the MAWI dataset, which contains captured raw network traffic from an Internet backbone link. Experimental results show that our machine learning model can achieve a high accuracy for network attacks which intensity is as low as 6% of the original traffic. This result is very promising, considering the fact that the amount of traffic in the Internet backbone link is substantial. This work also evaluates and quantifies the model performances on different attack types and the levels of intensity of these attacks. by Emily H. Do. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 65 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. An entropy-based approach to network attack classification with deep neural network Thesis application/pdf 2020-03-24T15:35:52Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="c8a99f01-99ca-4706-ab26-1c98fa76d339"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>An entropy-based approach to network attack classification with deep neural network</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Do, Emily H.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Detecting and classifying attacks on computer networks is a significant challenge for network providers and users. This thesis project builds a deep neural network to detect and classify network attacks. Our approach based on the hypothesis that each type of network attacks generates a distinguishable change in the entropies of certain features of network flows. To generate a training and validation dataset, synthetic attacks of different types and levels of intensity are injected to the MAWI dataset, which contains captured raw network traffic from an Internet backbone link. Experimental results show that our machine learning model can achieve a high accuracy for network attacks which intensity is as low as 6% of the original traffic. This result is very promising, considering the fact that the amount of traffic in the Internet backbone link is substantial. This work also evaluates and quantifies the model performances on different attack types and the levels of intensity of these attacks.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>