Cognitive discriminative feature selection using variance fractal dimension for the detection of cyber attacks

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dc.contributor.authorKaiser, Samilat
dc.contributor.examiningcommitteeMcLeod, Robert D. (Electrical and Computer Engineering) Mohammed, Noman (Computer Science)en_US
dc.contributor.supervisorFerens, Ken (Electrical and Computer Engineering)en_US
dc.date.accessioned2020-09-25T16:34:33Z
dc.date.available2020-09-25T16:34:33Z
dc.date.copyright2020-09-08
dc.date.issued2020-09-04en_US
dc.date.submitted2020-09-05T01:15:46Zen_US
dc.date.submitted2020-09-08T17:51:52Zen_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractThe dimensions of data have increased significantly with the abundance of the data that we share. This high-dimensional data results in redundant and irrelevant features that creates challenges to existing machine learning algorithms. Redundant and irrelevant features slow down the training and testing process as a result affecting the performance and run time of a learning algorithm. Conventional feature selection methods showcase great potential to select important features. However, machine enabled feature selection models are unable to solve complex analysis in absence of human cognitive aspect to it. This thesis addresses the challenge concerned with reducing the data dimensions for machine learning algorithms from cognitive aspect. The reduction is carried out by a variance fractal-based complexity analysis to select a reduced set of features of a cyber attack dataset for each attack type that are discriminative in nature. Furthermore, integrated artificial neural network were created with inputs that comprised of the reduced features selected through the complexity analysis. A performance comparison is also provided using our proposed methodology with resulting minimized dataset features of each attack types with non-minimized dataset. The comparative analysis shows that the resultant discriminative features derived from our proposed method not only consume less resource but also speed up the training and testing process while maintaining good detection rates.en_US
dc.description.noteOctober 2020en_US
dc.identifier.urihttp://hdl.handle.net/1993/35092
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectCognitive Intelligenceen_US
dc.subjectMachine Learningen_US
dc.subjectFractalsen_US
dc.subjectVariance Fractal Dimensionen_US
dc.subjectComplexity Analysisen_US
dc.subjectFeature Selectionen_US
dc.subjectCyber attacksen_US
dc.subjectCyber securityen_US
dc.subjectArtificial Neural Networken_US
dc.subjectDimensionalityen_US
dc.subjectNetwork Threatsen_US
dc.subjectCognitive computingen_US
dc.subjectComputational intelligenceen_US
dc.subjectDiscriminative featuresen_US
dc.titleCognitive discriminative feature selection using variance fractal dimension for the detection of cyber attacksen_US
dc.typemaster thesisen_US
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