1. A deep learning framework for predicting cyber attacks rates
- Author
-
Maochao Xu, Shouhuai Xu, Peng Zhao, and Xing Fang
- Subjects
GARCH ,lcsh:Computer engineering. Computer hardware ,Computer science ,0211 other engineering and technologies ,Weather forecasting ,lcsh:TK7885-7895 ,02 engineering and technology ,ARIMA ,Machine learning ,computer.software_genre ,RNN ,lcsh:QA75.5-76.95 ,Empirical research ,0202 electrical engineering, electronic engineering, information engineering ,Cyber threats ,021110 strategic, defence & security studies ,business.industry ,Deep learning ,020206 networking & telecommunications ,Hybrid models ,Computer Science Applications ,Recurrent neural network ,Signal Processing ,Cyber-attack ,lcsh:Electronic computers. Computer science ,Artificial intelligence ,LSTM ,business ,computer - 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.
- Published
- 2019