数据泄露风险的统计建模：识别和通知的时间
Statistical Modeling of Data Breach Risks: Time to Identification and Notification
摘要：
由于网络风险的复杂性，预测网络事件的成本非常具有挑战性。然而，对于提供网络保险的保险公司来说，这是不可避免的。识别事件的时间和注意到受影响个人的时间是确定网络事件成本的两个重要组成部分。在这项工作中，我们通过统计建模方法对这两个指标进行了初步研究。特别是，我们提出了一种新的方法来填补缺失数据，并进一步开发了一个依赖模型，以捕捉这两个指标所表现出的复杂模式。实证研究表明，该方法具有令人满意的预测性能，优于其他常用的模型。
英文摘要：
It is very challenging to predict the cost of a cyber incident owing to the complex nature of cyber risk. However, it is inevitable for insurance companies who offer cyber insurance policies. The time to identifying an incident and the time to noticing the affected individuals are two important components in determining the cost of a cyber incident. In this work, we initialize the study on those two metrics via statistical modeling approaches. Particularly, we propose a novel approach to imputing the missing data, and further develop a dependence model to capture the complex pattern exhibited by those two metrics. The empirical study shows that the proposed approach has a satisfactory predictive performance and is superior to other commonly used models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
