Knowledge Representation and Information Security

知识表示和信息安全

• 批准号: RGPIN-2019-04251
• 负责人: Hunter, Aaron
• 金额: $ 2.04万
• 依托单位: British Columbia Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737291
• 关键词: Knowledge; Representation; Information; Security

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.

该研究项目将侧重于使用正式人工智能（AI）社区的工具和方法来解决信息安全问题。我们对使用推理模型来识别和缓解与三个关键问题相关的漏洞特别感兴趣： - 信任：我们什么时候可以相信从另一个代理收到的信息，或者允许他们代表我们采取行动？ - 欺骗：我们如何识别特工是否正在积极试图操纵我们的信念？ - 隐私：如何确保私人数据在沟通中不被无意泄露？这些问题将通过开发沟通主体的知识和信念的明确的、声明性的模型来解决。我们将使用我们的模型来开发可以解决信息安全领域感兴趣的实际问题的工具。该程序属于知识表示和推理的范畴。  从根本上说，我们的方法是将安全通信视为推理问题：代理交换消息并从信息中得出结论。  对这种推理进行建模需要清楚地了解信任以及信念如何响应新信息而变化。  我们有几个不同的目标。  其中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，以捕获推理安全通信所需的关键概念。  第二个目标是实用的：我们希望实现能够有效解决我们开发的形式模型中的推理问题的软件工具。第三个目标已实现：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。这个研究项目是新颖的，因为它的目的是将正式的信仰模型从理想化的逻辑环境转移到信息安全的应用背景。这很重要，因为它连接了两个不同的研究社区。  一方面，我们拥有正式的人工智能研究社区，该社区已经对信念的动态进行了四十多年严格的正式研究。  虽然这项工作在理论上取得了进展，但尚未在实践层面得到广泛应用。  另一方面，我们有信息安全社区。  这只是一个研究领域，提供了大量具有重大实际意义的复杂问题。  因此，将这两个群体联系起来对双方来说都是有意义的，为正式人工智能提供重要的实际应用，同时为安全社区的难题提供新颖的解决方案。