Knowledge Representation and Information Security

该研究计划将专注于使用来自正式人工智能（AI）社区的工具和方法来解决信息安全问题。我们将特别感兴趣的是使用推理模型来识别和减轻与三个关键问题相关的漏洞： - 信任：我们何时可以相信从其他代理商收到的信息，或允许他们代表我们行事？ - 欺骗：我们如何识别一个代理人正在积极地试图操纵我们的信念？ - 隐私：我们如何确保私人数据不会在通信中无意中泄露？ 这些问题将通过开发明确的，声明性的模型的知识和信念的通信代理。我们将使用我们的模型来开发可以解决信息安全领域实际问题的工具。 该程序属于知识表示和推理的保护伞下福尔斯。 从根本上说，我们的方法是将安全通信视为一个推理问题：代理交换消息并从信息中得出结论。 建立这种推理模型需要对信任有一个清晰的理解，以及信念是如何随着新信息而改变的。 我们有几个明确的目标。 一个目标是理论上的：我们希望扩展现有的知识和信念模型，以捕获关于安全通信的推理所需的关键概念。 第二个目标是实际的：我们希望实现软件工具，可以有效地解决我们开发的正式模型中的推理问题。第三个目标是应用：我们希望使用我们的工具来推理信息安全中的难题。感兴趣的问题将通过与行业合作伙伴的互动来激发，重点是涉及信任和信念推理的实际问题。 这项研究计划是新颖的，因为它的目的是将形式化的信念模型从逻辑的理想化设置到信息安全的应用环境。这一点很重要，因为它连接了两个不同的研究社区。 一方面，我们有正式的人工智能研究社区，他们已经对信念的动态进行了40多年的严格的正式研究。 虽然这项工作在理论上取得了进展，但在实践中尚未得到广泛应用。 另一方面，我们有信息安全社区。 这只是一个研究领域，提供了大量的复杂问题，具有很大的实际意义。 因此，将这两个群体联系起来对双方都有意义，为正式的人工智能提供了重要的实际应用，同时为安全社区的难题提供了新的解决方案。

This research program will focus on using tools and methods from the formal Artificial Intelligence(AI) community to solve problems in Information Security. We will be particularly interested in using models of reasoning to identify and mitigate vulnerabilities related to three key issues: - Trust: When can we believe information received from another agent, or allow them to act on our behalf? - Deception: How can we identify when an agent is actively trying to manipulate our beliefs? - Privacy: How can we ensure that private data is not divulged inadvertently in communication? These issues will be addressed by developing explicit, declarative models of the knowledge and beliefs of communicating agents. We will use our models to develop tools that can solve practical problems of interest in the Information Security sector. This program falls under the umbrella of Knowledge Representation and Reasoning. Fundamentally, our approach is to view secure communication as a reasoning problem: agents exchange messages and draw conclusions from information. Modelling this kind of reasoning requires a clear understanding of trust, and how beliefs change in response to new information. We have several distinct goals. One goal is theoretical: we would like to extend existing models of knowledge and belief to capture key concepts required for reasoning about secure communication. The second goal is practical: we would like to implement software tools that can efficiently solve reasoning problems in the formal models that we have developed. The third goal is applied: we would like to use our tools to reason about difficult issues in Information Security. The problems of interest will be motivated by interaction with industry partners, with an emphasis on practical problems that involve reasoning about trust and belief. This research program is novel because it aims to move formal models of belief from the idealized setting of Logic to the applied context of Information Security. This is significant because it connects two distinct research communities. One one hand, we have the formal AI research community, which has produced over four decades of rigorous formal research on the dynamics of belief. While this work has advanced theoretically, it has not been applied extensively at a practical level. On the other hand, we have the Information Security community. This is simply a research area that offers an abundance of complex problems that are of great practical interest. Connecting these two groups therefore makes sense on both sides, providing important practical applications for formal AI while offering the hope of novel solutions to hard problems in the Security community.