| 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 |