Document Type


Publication Title

EURASIP Journal on Information Security

Publication Date



ARIMA, GARCH, RNN, Hybrid models, LSTM, Deep learning, BRNN-LSTM


Like how useful weather forecasting is, the capability of forecasting or predicting cyber threats can never be overestimated. Previous investigations show that cyber attack data exhibits interesting phenomena, such as long-range dependence and high nonlinearity, which impose a particular challenge on modeling and predicting cyber attack rates. Deviating from the statistical approach that is utilized in the literature, in this paper we develop a deep learning framework by utilizing the bi-directional recurrent neural networks with long short-term memory, dubbed BRNN-LSTM. Empirical study shows that BRNN-LSTM achieves a significantly higher prediction accuracy when compared with the statistical approach.


First published in EURASIP Journal on Information Security (2019) 2019:5.

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Data used in this work is not suitable for public use. The source code used in the present paper is available at analysis.