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Malware Classification into Families Based on File Contents and Characteristics

dc.contributor.authorMinjirbir, Jabir Shehu
dc.date.accessioned2020-12-16T10:54:31Z
dc.date.available2020-12-16T10:54:31Z
dc.date.issued2017-11-29
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/123456789/4968
dc.description.abstractThe use of malicious software (malware) as an instrument for carrying out different criminal activities both organised and non-organised have become the major threat faced by today’s world of connectivity. Frequency and complexity of such cyberattacks makes it difficult for computer antivirus companies to efficiently handle the high value of the new malwares released using traditional approaches that depends mainly on signature. As a result, machine learning approaches are now the best home for this problem, and have demonstrate a great success. One of the challenges now is finding a method that is reasonably fast, and can practically adopted. In this work, we try some of the best machine learning models Convolutional Neural Network (CNN) in one of the new computer generation language namely Julia.en_US
dc.description.sponsorshipAUST and AfDBen_US
dc.language.isoenen_US
dc.subjectProf. Lehel Csatoen_US
dc.subject2017 Computer Science Thesesen_US
dc.subjectMalware Classification into Families Based on File Contents and Characteristicsen_US
dc.subjectMinjibir Shehu Jabiren_US
dc.titleMalware Classification into Families Based on File Contents and Characteristicsen_US
dc.typeThesisen_US


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    This collection contains Computer Science Student's Theses from 2009-2019

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