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Implementation of Machine learning based Network Intrusion Detection System

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dc.contributor.author Mohammed, Alaa Ahmed Abdallah
dc.contributor.author Basheer, Alnazeer Ali
dc.contributor.author Saw, Mohammed Abdallah Ahmed
dc.contributor.author Haj nasir, Mohammed Shawgy Abdel rahman
dc.date.accessioned 2015-01-07T13:07:44Z
dc.date.available 2015-01-07T13:07:44Z
dc.date.issued 2014-09-10
dc.identifier.citation Mohammed,Alaa Ahmed Abdallah.Implementation of Machine learning based Network Intrusion Detection System/Alaa Ahmed Abdallah Mohammed ...{etal};Abuagla Babiker Mohammed.-Khartuom:Sudan University of Science and Technology, College of Engineering,2014.-55p.: ill ; 28cm.-Bachelors’Search en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/9664
dc.description Bachelors’Search en_US
dc.description.abstract Recently network attack has been spreaded in computer networks. They can Penetrates through user’s terminal and then cause damage to the system as general. This research aims to find solution for this trouble by implementing intrusion detection system which able to monitor the network traffic and analyse it to extract features, moreover using artificial tool Weka to classify the traffic (anomalous or not). in this research, several classification algorithms have been tested and evaluated to perform the task of detecting the intruders, among them J48 is considered the optimum choice reasonable accuracy 99.6122% with minimum testing time 2.9 sec. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject Electronics Engineering en_US
dc.subject Machine learning based en_US
dc.subject Network en_US
dc.subject Intrusion Detection en_US
dc.title Implementation of Machine learning based Network Intrusion Detection System en_US
dc.type Thesis en_US


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