EnvGuard：保障物联网环境中以环境为中心的安全与安保属性
EnvGuard: Guaranteeing Environment-Centric Safety and Security Properties in Web of Things
AI总结：
针对WoT环境中现有违规识别方法忽略环境复杂交互、缺乏用户意愿研究的问题，提出以环境为中心的EnvGuard方法，经实验验证其性能优于前沿方法，还构建了相关数据集。
摘要：
物联网（Web of Things, WoT）技术推动了日常环境中广泛部署的IoT设备的标准化集成，促进各类WoT应用实现环境的自动感知与调控。在WoT环境中，异构应用、用户活动与环境变化共同影响设备行为，存在意外违反安全与安保属性的风险。现有违规识别研究主要聚焦自动化应用分析，缺乏对环境中复杂交互的考量，且对用户违规解决策略的意愿研究不足。针对这些局限，我们提出EnvGuard——一种以环境为中心的WoT环境属性定制、违规识别与解决执行方法。我们在两个典型WoT环境中对EnvGuard进行了评估，通过用户研究与真实环境数据分析评估其性能，并基于采集数据构建数据集以支撑环境级违规识别。结果表明EnvGuard优于现有前沿方法，验证了其可用性、可行性与运行效率。
英文摘要：
Web of Things (WoT) technology facilitates the standardized integration of IoT devices ubiquitously deployed in daily environments, promoting diverse WoT applications to automatically sense and regulate the environment. In WoT environment, heterogeneous applications, user activities, and environment changes collectively influence device behaviors, posing risks of unexpected violations of safety and security properties. Existing work on violation identification primarily focuses on the analysis of automated applications, lacking consideration of the intricate interactions in the environment. Moreover, users' intention for violation resolving strategy is much less investigated. To address these limitations, we introduce EnvGuard, an environment-centric approach for property customizing, violation identification and resolution execution in WoT environment. We evaluated EnvGuard in two typical WoT environments. By conducting user studies and analyzing collected real-world environment data, we assess the performance of EnvGuard, and construct a dataset from the collected data to support environment-level violation identification. The results demonstrate the superiority of EnvGuard compared to previous state-of-the-art work, and confirm its usability, feasibility and runtime efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
