论文标题
通过通过新算法对重型数据进行建模的新算法来更好地抓住网络风险来建立网络弹性
Building up Cyber Resilience by Better Grasping Cyber Risk Via a New Algorithm for Modelling Heavy-Tailed Data
论文作者
论文摘要
网络安全和韧性是我们现代经济体中的主要挑战；这就是为什么他们是政府，安全和国防部队，公司和组织管理议程的首要任务的原因。因此，需要深入了解网络风险以提高弹性。我们在这里建议对{\ it Gendarmerie Nationale}提出的网络投诉的数据库进行分析。我们使用针对非负不对称重尾数据开发的新算法进行了此分析，该算法可以成为应用领域中的方便工具。该方法可以很好地估计包括尾巴在内的完整分布。我们的研究证实了损失期望的有限性，可保险性的必要条件。最后，我们提出了该模型的风险管理后果，将其结果与其他标准EVT模型进行比较，并为基于尾巴的脂肪的攻击奠定了基础。
Cyber security and resilience are major challenges in our modern economies; this is why they are top priorities on the agenda of governments, security and defense forces, management of companies and organizations. Hence, the need of a deep understanding of cyber risks to improve resilience. We propose here an analysis of the database of the cyber complaints filed at the {\it Gendarmerie Nationale}. We perform this analysis with a new algorithm developed for non-negative asymmetric heavy-tailed data, which could become a handy tool in applied fields. This method gives a good estimation of the full distribution including the tail. Our study confirms the finiteness of the loss expectation, necessary condition for insurability. Finally, we draw the consequences of this model for risk management, compare its results to other standard EVT models, and lay the ground for a classification of attacks based on the fatness of the tail.
