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Anomaly detection framework for big data from Ghana perspective

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dc.contributor.author Gyamfi N.K.
dc.contributor.author Appiah P.
dc.contributor.author Aidoo A.
dc.date.accessioned 2022-10-31T15:05:36Z
dc.date.available 2022-10-31T15:05:36Z
dc.date.issued 2018
dc.identifier.issn 20780958
dc.identifier.uri http://41.74.91.244:8080/handle/123456789/485
dc.description Gyamfi, N.K., Kumasi Technical University, Department of Computer Science, Ghana; Appiah, P., Department of Information Technology Education, University of Education Winneba, College of Technology Education, Kumasi, Ghana; Aidoo, A., Eastern Connecticut State University, 83 Windham Street, Willimantic, CT 06226, United States en_US
dc.description.abstract An anomaly (deviant objects, exceptions, peculiar objects) is an important concept of the analysis. The volume and velocity of the data within many systems makes it difficult to detect and process anomalies for Big Data in real-time. Many anomaly detective systems count on the historical data for detecting behaviors�. Considering it as a problem to financial institutions in Ghana, the researcher proposed robust anomaly detection framework. The proposed frame work defines Spark stream, as part of Spark ecosystem, which stream data in real-time. Also, the proposed framework data model was build using SVM, Linear regression and Logistic regression as a package found in Spark MLlib. Additionally, the proposed framework was explained clearly to be implemented in real systems for financial institutions. � 2018 Newswood Limited. en_US
dc.publisher Newswood Limited en_US
dc.subject Anomaly en_US
dc.subject Anomaly detection en_US
dc.subject Framework en_US
dc.subject MLlib en_US
dc.subject Real-Time en_US
dc.subject Spark ecosystem en_US
dc.subject Support Vector Machine en_US
dc.title Anomaly detection framework for big data from Ghana perspective en_US
dc.type Conference Paper en_US


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