Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Risk Anal. 2018 Feb;38(2):226-241. doi: 10.1111/risa.12844. Epub 2017 Jul 5.
Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system-based for high-consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward-looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high-consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents.
在组织中管理网络安全涉及在一系列可能的选项中分配保护预算。这需要评估这些选项的收益和成本。这里提出的风险分析在相关数据可用时是统计性的，对于尚未发生的高后果事件则是基于系统的。本文首先提出了一个组织中网络安全的通用概率风险分析框架，然后描述了三个由最近网络攻击引发的前瞻性分析示例。第一个示例是对实际数据库的统计分析，通过对可能发生的高后果攻击场景的贝叶斯分析扩展了损失分布的上限，这些场景可能在未来发生。第二个示例是对智能互联电网的网络风险的系统分析，表明存在一个最佳的连接水平。第三个示例是对现有网络安全系统的软件进行升级或采用新系统以领先于试图找到进入途径的对手的连续决策的分析。结果是在考虑和不考虑一些支持基于过去数据和预期事件的风险管理决策的对策的情况下，网络攻击造成的损失分布。
