CT-ER: Collaborative Research: MiMANSaS: Metrics, Models and Analysis of Network Security and Survivability

信息和通信技术（ICT）基础设施故障和网络攻击是可能造成灾难性社会影响的现实。信息保障（IA）可以定义为通过确保信息和通信技术系统的可靠性和安全性来保护和防御信息和通信技术系统的行动。迫切需要系统的内部信息分析方法，使ICT系统能够适应和生存任何类型的中断或攻击。IA技术发展的一个主要障碍是缺乏能够确定IA机制有效性的模型和度量。这个探索性项目为来自不同机构的三个pi之间的合作努力奠定了基础：杜克大学、密苏里大学堪萨斯城分校和匹兹堡大学，他们专注于开发度量和模型，这些模型将允许人们定量地研究信息通信技术基础设施网络组件的信息保障（IA）的技术方面。该方法的基础是将攻击树、攻击图、特权图和故障树统一到一个通用的可扩展框架中，该框架具有一组定义良好的指标和应用场景。通过马尔可夫链和随机Petri网对基本模型进行扩展，包括状态信息、随机属性和奖励，从而实现更广泛的攻击和故障场景。所开发的模型和度量的影响在于，它们提供了确定IA机制有效性所需的技术和工具，并允许人们检测瓶颈，并评估信息保证、性能和成本级别之间的权衡。

Information and Communications Technology (ICT) infrastructure failures and cyber attacks are realities that can have catastrophic societal effects. Information Assurance (IA) can be defined as the operations undertaken to protect and defend ICT systems by ensuring their dependability and security. There is a critical need for systematic IA methods that enable ICT systems to adapt and survive any type of disruption or attack. A major hurdle in the development of IA techniques is the lack of models and metrics which enable one to determine the effectiveness of IA mechanisms. This exploratory project seeds a collaborative effort between three PIs at different institutions: Duke University, University of Missouri Kansas-City, and the University of Pittsburgh focused on the development of metrics and models that will allow one to quantitatively study the technical aspects of information assurance (IA) for the network component of the ICT infrastructure. The basis of the approach is to unify attack trees, attack graphs, privilege graphs and fault trees into a common scalable framework with a well defined set of metrics and application scenarios. Extensions of the basic model that include state information, stochastic properties and rewards via Markov chains and stochastic Petri nets, enabling a wider variety of attack and fault scenarios are being studied. The impact of the models and metrics developed is that they provide the techniques and tools necessary to determine the effectiveness of IA mechanisms and allow one to detect bottlenecks and to evaluate the tradeoffs between levels of information assurance, performance and cost.