TY - JOUR AU - Fang, Xing AU - Xu, Maochao AU - Xu, Shouhuai AU - Zhao, Peng PY - 2019 DA - 2019/05/22 TI - A deep learning framework for predicting cyber attacks rates JO - EURASIP Journal on Information Security SP - 5 VL - 2019 IS - 1 AB - 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. SN - 2510-523X UR - https://doi.org/10.1186/s13635-019-0090-6 DO - 10.1186/s13635-019-0090-6 ID - Fang2019 ER -