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 Gendarmerie Nationale. We perform this analysis with a new algorithm developed for non-negative asymmetric heavy-tailed data, which could become a handy tool for applied fields, including operations research. 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.

网络安全和弹性是我们现代经济的主要挑战；这就是为什么它们是政府、安全和国防部队、公司和组织管理层议程上的首要优先事项。因此，需要深入了解网络风险以提高抵御能力。我们在此建议对国家宪兵队提出的网络投诉数据库进行分析。我们使用为非负不对称重尾数据开发的新算法来执行此分析，该算法可能成为包括运筹学在内的应用领域的便捷工具。该方法可以很好地估计包括尾部在内的完整分布。我们的研究证实了损失预期的有限性，这是可保性的必要条件。最后，我们得出该模型对风险管理的影响，将其结果与其他标准 EVT 模型进行比较，并为基于尾部肥厚的攻击分类奠定基础。