Reducing the Observable States Space of Hidden Markov Model for Distributed Denial of Service Attack Prediction using Kullback-liebler Divergence

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dc.contributor.authorAfolorunso, A.A
dc.contributor.authorAdewole, A.P
dc.contributor.authorAbass, O
dc.contributor.authorLonge, H.O
dc.date.accessioned2019-02-27T12:42:53Z
dc.date.available2019-02-27T12:42:53Z
dc.date.issued2017
dc.descriptionJournal Articlesen_US
dc.description.abstractDistributed Denial of Service (DDoS) attack floods the network with loads of unwanted packets and requests that weigh down the system resources such as memory and processors. Hidden Markov model (HMM) is one of the models that can be used to predict and detect such attacks. A problem to be solved was determining the observable states and subsequently, the model parameters since the performance of the model depends on the accurate selection of these parameters. In this work, the concept of entropy was used to determine the observable states, which characterise the HMM. In order to improve computational efficiency of the algorithm for estimating the parameters of the model, Kullback-Liebler Divergence (KLD) method was employed for reducing and selecting appropriate observable states to achieve a good prediction model. The experimental results justified the suitability of KLD in reducing the entropy-based observable states of HMM for predicting DDoS attack.en_US
dc.description.sponsorshipTertiary Education Trust Funden_US
dc.identifier.citationAfolorunso, A.A, Adewole, A.P, Abass, O and Longe, H.O (2017). Reducing the Observable States Space of Hidden Markov Model for Distributed Denial of Service Attack Prediction using Kullback-liebler Divergence. Unilag Journal of Medicine, Science and Technology, Vol.5(1), 137-151p.en_US
dc.identifier.issn2408-5049
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/3863
dc.language.isoenen_US
dc.publisherUniversity of Lagos Press, Akokaen_US
dc.relation.ispartofseriesUniversity of lagos Journal;Vol.5(1)
dc.subjectKullback-Liebler Divergence (KLD) methoden_US
dc.subjectDistributed Denial of Serviceen_US
dc.subjectHidden Markov model (HMM)en_US
dc.subjectPrediction modelen_US
dc.titleReducing the Observable States Space of Hidden Markov Model for Distributed Denial of Service Attack Prediction using Kullback-liebler Divergenceen_US
dc.typeArticleen_US
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Volume 5, Issue 1, 2017