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UOT 004
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Biblioqrafik təsvir | Alguliyev , R.M. Hybridisation of classifiers for anomaly detection in big data / R.M. Alguliyev , R.M. Aliguliyev , F.D. Abdullayeva // International Journal of Big Data Intelligence. - 2019. - N: Vol. 6, No. 1,.- P. 11-19. | Annotasiya | Recently, the widespread use of cloud technologies has led to the rapid increase in the
scale and complexity of this infrastructure. The degradation and downtimes in the performance
metrics of these large-scale systems are considered to be a major problem. The key issue in
addressing these problems is to detect anomalies that can occur in hardware, software and state of
the systems of cloud infrastructure. In this paper, for the detection of anomalies in performance
metrics of cloud infrastructure, a semi-supervised classification method based on an ensemble of
classifiers is proposed. In the proposed method, to build ensemble Naive Bayes, J48, SMO,
multilayer perceptron, IBK and PART algorithms are used. To detect anomalous behaviour on
the performance metrics the public data of the Google and Yahoo! companies, Python 2.7,
MATLAB, Weka and Google Cloud SDK Shell applications are used. In the result of the
experimental study of the model, 90% detection accuracy is obtained. | Elektron variant | Elektron variant |
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