Collaborative Research: CPS: Medium: Enabling Data-Driven Security and Safety Analyses for Cyber-Physical Systems

协作研究：CPS：中：为网络物理系统实现数据驱动的安全和安全分析

• 批准号: 2414176
• 负责人: Kevin Moran
• 金额: $ 38.24万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2414176&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Enabling; Collaborative; Research; CPS; Medium; Enabling

Smart home products have become extremely popular with consumers due to the convenience offered through home automation. In bridging the cyber-physical gap, however, home automation brings a widening of the cyber attack surface of the home. Research towards analyzing and preventing security and safety failures in a smart home faces a fundamental obstacle in practice: the poor characterization of home automation usage. That is, without the knowledge of how users automate their homes, it is difficult to address several critical challenges in designing and analyzing security systems, potentially rendering solutions ineffective in actual deployments. This project aims to bridge this gap, and provide researchers, end-users, and system designers with the means to collect, generate, and analyze realistic examples of home automation usage. This approach builds upon a unique characteristic of emerging smart home platforms: the presence of "user-driven" automation in the form of trigger-action programs that users configure via platform-provided user interfaces. In particular, this project devises methods to capture and model such user-driven home automation to generate statistically significant and useful usage scenarios. The techniques that will be developed during the course of this project will allow researchers and practitioners to analyze various security, safety and privacy properties of the cyber-physical systems that comprise modern smart homes, ultimately leading to deployments of smart home Internet of Things (IoT) devices that are more secure. The project will also produce and disseminate educational materials on best practices for developing secure software with an emphasis on IoT devices, suitable for integration into existing computer literacy courses at all levels of education. In addition, the project will focus on recruiting and retaining computer science students from traditionally underrepresented categories. This project is centered on three specific goals. First, it will develop novel data collection strategies that allow end-users to easily specify routines in a flexible manner, as well as techniques based on Natural language Processing (NLP) for automatically processing and transforming the data into a format suitable for modeling. Second, it will introduce approaches for transforming routines into realistic home automation event sequences, understanding their latent properties and modeling them using well-understood language modeling techniques. Third, it will contextualize the smart home usage models to make predictions that cater to security analyses specifically and develop tools that allow for the inspection of a smart home’s state alongside the execution of predicted event sequences on real products. The techniques and models developed during the course of this project will be validated with industry partners and are expected to become instrumental for developers and researchers to understand security and privacy properties of smart homes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于家庭自动化提供的便利性，智能家居产品在消费者中变得非常受欢迎。然而，在弥合网络物理差距时，家庭自动化带来了房屋的网络攻击表面的扩大。研究和防止智能家居中的安全性和安全性故障的研究面临实践中的根本障碍：家庭自动化使用情况的不良表征。也就是说，在不了解用户如何自动化房屋的情况下，很难在设计和分析安全系统时面临一些关键的挑战，从而使解决方案在实际部署中无效。该项目旨在弥合这一差距，并为研究人员，最终用户和系统设计师提供收集，生成和分析家庭自动化使用量的现实示例的手段。这种方法建立在新兴智能家居平台的独特特征上：以触发动作程序的形式存在“用户驱动”自动化，该程序通过平台提供的用户界面配置了用户配置。特别是，该项目设计了捕获和建模此类用户驱动的家庭自动化的方法，以生成具有统计学意义和有用的用法方案。在该项目过程中将开发的技术将使研究人员和从业人员能够分析完成现代智能家居的网络物理系统的各种安全性，安全性和隐私性，最终导致了更安全的智能家庭互联网（IoT）设备的部署。该项目还将生产并传播有关开发安全软件的最佳实践的教育材料，并重点介绍物联网设备，适合在各个级别的教育中集成到现有的计算机识字课程中。此外，该项目将着重于传统代表性不足类别的招募和保留计算机科学专业的学生。该项目以三个具体目标为中心。首先，它将制定新颖的数据收集策略，使最终用户可以灵活地轻松指定例程，以及基于自然语言处理（NLP）的技术，用于自动处理并将数据转换为适合建模的格式。其次，它将引入将例程转换为现实的家庭自动化事件序列，了解其潜在属性并使用良好理解的语言建模技术对其进行建模的方法。第三，它将将智能房屋使用模型进行环境化，以进行预测，以迎合安全分析并开发工具，并允许在执行真实产品的预测事件序列的情况下检查智能家庭的状态。在该项目过程中开发的技术和模型将通过行业合作伙伴进行验证，并有望使开发人员和研究人员了解智能家居的安全和隐私属性。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来获得的支持。