Collaborative Research: Network Protection under Uncertainty

该研究项目的重点是在恐怖主义威胁的情况下提高人口的安全，以及如何使用综合建模（1）提高对人类在对抗环境中决策的科学理解，以及（2）设计分配政策和保护软目标的最佳实践，即，公共交通设施、群众聚集场所及其使用者。它涉及理论和计算研究，将通过综合建模和优化探索基础设施保护技术的基础，并将开发新的方法和工具来支持实时应急管理和政策制定。该项目具有广泛的应用范围，从国土安全和执法到军事应用。这项研究是变革性的，因为它为风险管理和传感/控制网络安全带来了新的视野。预计研究的最重要影响将是公共设施、校园、学校建筑及其用户，为第一反应者提供有关预防和威慑恐怖袭击以及尽量减少袭击期间损害的决策支持。工作结果将与主要的区域参与者进行沟通，包括纽约/新泽西州港务局、新泽西州交通局、新泽西州国土安全部和新泽西州交通部。 该研究产生的对抗性决策模型和虚拟游戏基础设施也适用于其他领域，例如培训执法人员以防止帮派暴力和培训校园警察。这项研究还旨在产生广泛的影响，特别是在研究和教育的整合方面;它将为博士学位提供基础。博士论文，并将提供支持，以指导博士后在信息科学及其应用安全。研究结果将为学生和从业者使用的基础设施保护游戏综合书籍建立框架。研究项目的主要特点是（i）封闭形式的分析解决方案，（ii）游戏模型，探索玩家利用对手类型，攻击类型和力量的不确定性和不完整信息的方式，（iii）结合检测概率和表示移动目标的人流模型的巡逻游戏，以及（iv）构建在沉浸式虚拟世界内的虚拟游戏，其被设计为收集关于玩家行为的数据并使用诸如预期损害、不成功攻击的分数、研究的目标是：（1）开发随机网络保护博弈算法，供防御者使用，与现有技术一起，规划应急准备，预防恐怖主义或网络攻击，以减少宝贵资产的损失;（2）设计一个包含上述模型关键元素的虚拟游戏，以收集关于游戏中不同玩家行为的信息;（3）通过虚拟游戏在受控环境中测试真实的玩家行为的模型，以验证和完善随机游戏中人类主体行为的模型。这些目标将通过使资源分配游戏模型适应虚拟游戏来实现。将收集真实的玩家行为数据来验证模型。这项研究的预期结果是分析和相关的从业人员，如应急管理机构，警察等的研究作出贡献i）在人为紧急情况下的对抗性政策的随机建模; ii）部分可观察系统的随机博弈论; iii）在保卫关键基础设施的决策; iv）在国土安全和资源分配教育未来的第一响应者。

This research project focuses on improving the security of the population in the case of a terrorist threat, and how integrated modeling can be used (1) to improve scientific understanding of human decision-making in adversarial contexts, and (2) in the design of allocation policies and best practices for protection of soft targets, i.e., public transit facilities, places of mass gathering, and their users. It involves theoretical and computational research that will explore the foundations of infrastructure protection technologies through integrated modeling and optimization and will develop new methods and tools to support real-time emergency management and policy development. The project has a wide range of applications from homeland security and law enforcement to military applications. This research is transformational since it brings a fresh vision to risk management and sensing/control networks for security. The most significant impact of the research is expected to be to public facilities, campuses, school buildings and its users by providing support to first responders in decision making about prevention and deterrence of terrorist attacks as well as minimization of damage during an attack. Results of the work will be communicated with the major regional players, including NY/NJ Port Authority, NJ Transit, NJ Department of Homeland Security and NJDOT. The adversarial decision making models and virtual game infrastructure that will result from the research are also applicable to other areas such as training law enforcement personnel to prevent gang violence and training campus police. This study also seeks to have a broad impact, especially, in regard to the integration of research and education; it will provide the basis for a Ph.D. dissertation and will provide support to mentor a postdoctoral associate in information sciences and its application to security. The results of the research will establish the framework for a comprehensive book on infrastructure protection games to be used by students as well as practitioners.The key features of the research project are (i) closed form analytical solutions, (ii) game models that explore the ways in which the uncertainty and incomplete information about the adversary type, attack type and power, can be exploited by the players, (iii) a patrolling game that incorporates detection probability and a people flow model representing moving targets, and (iv) a virtual game built inside an immersive virtual world designed to both collect data about the players' behavior and to validate the game models using metrics such as the expected damage, the fraction of unsuccessful attacks, etc. The objectives of the research are to (1) Develop stochastic network protection game algorithms to be used by the defenders, together with current technologies, in planning for emergency preparedness, for prevention of terrorist or cyber attacks to reduce the loss of valuable assets; (2) Design a virtual game involving the key elements of the above models in order to collect information about the behavior of various players of the game; (3) Test models for real player behavior in a controlled setting via virtual games to verify and refine models for human subject behavior in the stochastic games. These objectives will be accomplished by adapting resource allocation game models to the virtual game. Realistic player behavior data will be collected to validate the models. The anticipated results of this research are both analytical and relevant to practitioners, such as emergency management agencies, police, etc. The research makes contributions to i) stochastic modeling of adversarial policies in man-made emergencies; ii) stochastic game theory for partially observable systems; iii) decision-making in defending critical infrastructure; iv) educating future first responders in homeland security and resource allocation.