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.

中文翻译：

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预测网络攻击率的深度学习框架

就像天气预报多么有用一样，永远也不能高估预测或预测网络威胁的能力。先前的研究表明，网络攻击数据表现出有趣的现象，例如长期依赖性和高度非线性，这对建模和预测网络攻击率提出了特殊的挑战。与文献中使用的统计方法不同，在本文中，我们通过利用具有较长短期记忆的双向递归神经网络（称为BRNN-LSTM）开发了深度学习框架。实证研究表明，与统计方法相比，BRNN-LSTM实现了更高的预测准确性。