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A deep learning framework for predicting cyber attacks rates
- Source :
- EURASIP Journal on Information Security, Vol 2019, Iss 1, Pp 1-11 (2019)
- Publication Year :
- 2019
- Publisher :
- SpringerOpen, 2019.
-
Abstract
- Abstract 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.
Details
- Language :
- English
- ISSN :
- 2510523X
- Volume :
- 2019
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- EURASIP Journal on Information Security
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.fe42097297b04ab8ba780ea2b1db7f4a
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s13635-019-0090-6