Keeping Secrets: Realizing the Potential of Decentralized Discrete-Event Systems

拟议的研究计划将开发分散的离散事件系统(DES)，以通过确保安全性和不透明性来支持自动驾驶车辆(AV)、智能城市、电网和其他相互连接的通信代理网络。发电和发电正在变得分散，自动驾驶汽车本质上也是分散的。分散化需要在不同的代理(例如，AVS、发电机或计算机)之间交流信息(如事件发生)，这会使系统面临可能的安全漏洞或恶意操作。拟议的计划将开发保持系统不透明度的新技术；在DES中，不透明度意味着已被指定为机密的事件或状态序列无法与其他非机密事件序列或状态区分开来。如果一个系统是不透明的，敌对特工将不会拥有对该系统发动攻击至关重要的信息。为了让分布在整个系统中的代理协同工作以确保安全性，一个代理所缺乏的信息或知识必须由另一个代理提供(在本例中为通信)，并提供不同的和互补的信息。虽然DES之前的工作已经检查了代理之间的通信以实施控制，但现有的研究还没有集中在确保不透明度的通信上。分布式通信的问题是，代理必须找到方法来帮助其他代理执行任务，同时确保对手无法充分了解系统以辨别秘密信息。推导通信策略需要了解每个代理拥有什么信息。就像人们不仅根据他们的直接观察做出决定一样，自动化代理也应该根据他们对其他代理的知识做出决定。因此，拟议的研究还将为DES开发一个数理逻辑模型，该模型捕获代理关于系统和其他代理的知识。知识的数学模型已经被用于计算机科学中的问题建模，但还没有在控制系统或DES中得到显著的利用。举个例子，为了让“智能汽车”真正智能，我们需要让这些无人驾驶汽车具备推理其他车辆所拥有的知识的能力，这样车辆就可以独立地决定发送什么信息以及何时发送，确保系统不会被未经授权的代理征用。我的研究计划将在DES建模、算法设计和软件开发方面培训4名博士生和3名MASC学生，并将使HQP能够测试和完善他们对互联计算机和自动驾驶车辆的真实网络的研究。我的计划将使加拿大能够满足社会需求，确保在我们转向更分布式、更智能和更自主的系统时，我们不会牺牲安全和安全。

The proposed research program will develop decentralized discrete-event systems (DES) to support autonomous vehicles (AV), smart cities, power grids and other interconnected networks of communicating agents by ensuring security and opacity. Power and electricity generation is becoming decentralized and self-driving cars are by nature decentralized. Decentralization requires the communication of information (such as event occurrences) between disparate agents (e.g., AVs, power generators, or computers), which opens a system to possible security breaches or hostile maneuvering. The proposed program will develop new techniques for maintaining system opacity; in DES, opacity means that sequences of events or states that have been designated as secret cannot be distinguished from other, non-secret event sequences or states. If a system is opaque, hostile agents will not have information critical to mounting an attack on the system. For agents that are distributed across a system to work together to ensure security, information or knowledge that one agent lacks must be supplied (in this case, communicated) by another agent with different, and complementary, information. While prior work in DES has examined communication between agents to enact control, existing research has not focused on communication to ensure opacity. The problem with distributed communication is that agents must find ways to help other agents perform tasks while ensuring that an adversary does not learn enough about the system to discern secret information. Deriving communication strategies requires understanding what information each agent possesses. Just as people make decisions based not only on their direct observations but also on what they know about what other people know, so too should automated agents make decisions based on their knowledge of other agents. Consequently, the proposed research will also develop a mathematical-logic model for DES that captures the knowledge that agents have about the system and about other agents. Mathematical models of knowledge have been used, to advantage, to model problems in computer science but have yet to be significantly exploited in control systems or DES. As an example, to make "smart cars" truly smart we need to imbue these unmanned vehicles with the capability to reason about the knowledge that other vehicles have so that vehicles can independently determine what information to send and when to send it in a way that ensures that the system is impervious to being commandeered by unauthorized agents. My research program will train 4 PhD and 3 MASc students in DES modeling, algorithm design, and software development and will enable HQP to test and refine their research on real networks of interconnected computers and autonomous vehicles. My program will enable Canada to meet societal demands for ensuring that as we move to more distributed, more intelligent and more autonomous systems, we do not sacrifice security and safety.