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Cyber risk ordering with rank-based statistical models

机译:网络风险排序与基于秩的统计模型

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In a world that is increasingly connected on-line, cyber risks become critical. Cyber risk management is very difficult, as cyber loss data are typically not disclosed. To mitigate the reputational risks associated with their disclosure, loss data may be collected in terms of ordered severity levels. However, to date, there are no risk models for ordinal cyber data. We fill the gap, proposing a rank-based statistical model aimed at predicting the severity levels of cyber risks. The application of our approach to a real-world case shows that the proposed models are, while statistically sound, simple to implement and interpret.
机译:在一个越来越多地连接在线的世界中,网络风险变得至关重要。 网络风险管理非常困难,因为通常未公开网络丢失数据。 为了减轻与其披露相关的声誉风险,可以根据有序严重程度收集损失数据。 但是,迄今为止,序数网络数据没有风险模型。 我们填补了差距,提出了一种基于秩的统计模型,旨在预测网络风险的严重程度。 我们对真实案例的方法在真实案例中的应用表明,拟议的型号是统计上的,易于实施和解释。

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