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A smart network intrusion detection system based on network data analyzer and support vector machine

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dc.citedby4
dc.contributor.authorBabatunde O.S.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorMostafa S.A.en_US
dc.contributor.authorFoozy C.F.M.en_US
dc.contributor.authorKhalaf B.A.en_US
dc.contributor.authorFadel A.H.en_US
dc.contributor.authorShamala P.en_US
dc.contributor.authorid57219411278en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid37036085800en_US
dc.contributor.authorid56380430100en_US
dc.contributor.authorid57205359430en_US
dc.contributor.authorid57219163717en_US
dc.contributor.authorid56345862600en_US
dc.date.accessioned2023-05-29T08:12:29Z
dc.date.available2023-05-29T08:12:29Z
dc.date.issued2020
dc.description.abstractBecause of the critical interest for viable IDS in networks security, the researchers are trying to recognize enhanced methods. This work shows how the KDD dataset is exceptionally helpful for testing distinctive DDoS classifiers. Conclusively, there are two principal ways to reduce the classification complexity and improve the DDoS attack detection accuracy by using nonlinear Support Vector Machine (SVM)s: (1) reducing the number of support vectors; (2) simplifying the classification process for special kernels. This paper proposes a Smart Intrusion Detection System (SIDS) that integrates a Network Data Analyzer (NDA) and SVM to reduce the computation iterations needed by the SVM by eliminating the presumed attack types before performing the classification process. Reduction in data can also serve as a way to increase speed and reduce time in computations. Also, it enhances performance evaluation as 3 types of attack are easier to evaluate than 4 types especially where the 4th type is dominant in the analyzed datasets (the case of DDoS attack being about 79% of the total dataset). As experimented, the proposed Smart Intrusion Detection System method has shown a way in dataset reduction by simply eliminating the DDOS attack types with the same amount of data as compared to Batch 2. Batch 1 serves as a control experiment as indicated by its good performance evaluation measurements. � 2020, World Academy of Research in Science and Engineering. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.30534/ijeter/2020/3381.12020
dc.identifier.epage220
dc.identifier.issue1 Special Issue 1
dc.identifier.scopus2-s2.0-85092604509
dc.identifier.spage213
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85092604509&doi=10.30534%2fijeter%2f2020%2f3381.12020&partnerID=40&md5=d982618b5d224490c9c6e20524348ef8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25668
dc.identifier.volume8
dc.publisherWorld Academy of Research in Science and Engineeringen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Journal of Emerging Trends in Engineering Research
dc.titleA smart network intrusion detection system based on network data analyzer and support vector machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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