TWC: Small: Scalable Hybrid Attack Graph Modeling and Analysis

TWC：小型：可扩展的混合攻击图建模和分析

• 批准号: 1524940
• 负责人: John Hale
• 金额: $ 48.84万
• 依托单位: University of Tulsa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1524940&HistoricalAwards=false
• 关键词: TWC; Small; Scalable; Hybrid; Attack

Cyber-physical systems (CPSs) operate nearly all of society's critical infrastructures (e.g., energy, transportation and medicine). In performing mission critical functions, CPSs exhibit hybrid (both discrete and continuous) behavior as they use digital technology to control and monitor physical processes. CPS security analysis is particularly challenging because an attacker can make use of a wide variety of vulnerabilities in the digital elements of the system (e.g., the network), the physical elements of the system, or some combination. This project is developing a mathematical and computational framework for modeling and analyzing large, complex CPSs to capture their vulnerabilities and the resulting attack paths (steps an attacker might use to disrupt the system).This project is developing hybrid attack graphs (HAGs) as a mathematical formalism for representing security properties and compound exposures in CPSs. HAGs reflect a functional view of exposures, capturing state transitions over CPSs due to the execution of exploits in either the cyber or physical domains. As such, they offer the potential to comprehensively document a CPS attack surface. The researchers are designing HAG generation algorithms that apply intelligent search and parallelization strategies and creating a suite of web-based tools to cope with the computational burdens of large-scale CPS attack surface modeling. The project is developing an array of analytical methods, refined based on Markov Processes, classic reachability, and other techniques. To provide an experimentation platform for evaluating the project's tools and techniques, the researchers are building a CPS test bed comprised of network-controlled robotic vehicles. The testbed will also provide a competitive learning environment in which to teach students about CPS security principles in a fun and engaging manner.

网络物理系统（CPS）操作几乎所有社会的关键基础设施（例如，能源、交通和医药）。 在执行使命关键功能时，CPS表现出混合（离散和连续）行为，因为它们使用数字技术来控制和监视物理过程。 CPS安全分析特别具有挑战性，因为攻击者可以利用系统的数字元件中的各种漏洞（例如，网络）、系统的物理元件或某种组合。该项目正在开发一个数学和计算框架，用于建模和分析大型复杂的CPS，以捕获其漏洞和由此产生的攻击路径（攻击者可能用于破坏系统的步骤）。该项目正在开发混合攻击图（HAGs）作为表示CPS中安全属性和复合暴露的数学形式。 HAG反映了风险的功能视图，捕获由于在网络或物理域中执行漏洞而导致的CPS上的状态转换。 因此，它们提供了全面记录CPS攻击面的可能性。 研究人员正在设计应用智能搜索和并行化策略的HAG生成算法，并创建一套基于Web的工具来科普大规模CPS攻击面建模的计算负担。 该项目正在开发一系列分析方法，这些方法基于马尔可夫过程、经典可达性和其他技术进行了改进。 为了提供一个实验平台来评估该项目的工具和技术，研究人员正在建立一个由网络控制的机器人车辆组成的CPS测试床。 该测试平台还将提供一个有竞争力的学习环境，以有趣和引人入胜的方式向学生传授CPS安全原则。