Neural Network Prediction of Self-Similarity Network Traffic

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dc.contributor.authorIkharo, A.B.
dc.contributor.authorAnyachebelu, Tochukwu Kene
dc.date.accessioned2023-12-14T07:04:44Z
dc.date.available2023-12-14T07:04:44Z
dc.date.issued2022-12-02
dc.description.abstractSeveral factors are found to influence either short or long-term burstiness in Transmission Control Protocol (TCP) flow across many networking facilities and services. Predicting such self-similar traffic has become necessary to achieve better performance. In this study, ANN model was deployed to simulate College Campus network traffic. A Feed Forward Backpropagation Artificial Neural Network (ANN) and Wireshark tools were implemented to study the network Scenario. The predicted series were then compared with the corresponding real traffic series (Mobile Telephone-Network (MTN)-Nigeria). Suitable performance measurements of the Means Square Error (MSE) and the Regression Coefficient were used. Our results showed that burstiness is present in the network across many time scales. With the increasing number of data packet distributions thereby providing a steady flow of burst over the entire period of system load as the traffic network performance improves.en_US
dc.identifier.citationAnyachebelu, T.K. & Ikharo, A.B. (2022) Neural Network Prediction of Self-Similarity Network Trafficen_US
dc.identifier.urihttps://keffi.nsuk.edu.ng/handle/20.500.14448/5555
dc.language.isoenen_US
dc.publisherDepartment of Computer Science, Nasarawa State University Keffien_US
dc.subjectBurstiness, Self-similar, Network traffic, Performance, Simulation, Artificial Neural Network, Packeten_US
dc.titleNeural Network Prediction of Self-Similarity Network Trafficen_US
dc.typeArticleen_US

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