CRII: SaTC: Transparent Capture and Aggregation of Secure Data Provenance for Smart Devices

计算机越来越普及和多样化，嵌入到从智能手机和可穿戴计算机到家庭自动化设备和汽车系统的各种设备中。这种爆炸式增长的速度远远超过了设备行为的分析和理解速度，为“物联网”设备从事侵犯用户隐私或传播恶意软件等恶意活动创造了前所未有的机会。该项目正在设计和实施跟踪智能设备系统中数据处理和通信的来源（即历史）的新框架。为了便于识别恶意行为，该项目正在开发用于提取设备来源的非侵入性技术，并提供一个公共问责基础设施，通过该基础设施可以分析智能设备之间的交互历史。在这种环境下，由于计算平台的多样性，很难有效地提取细粒度的数据来源。为了克服这些挑战，研究人员正在设计和实施微创机制和相关算法，以便在多个系统层观察智能设备的活动。预期的结果包括开发一种改进机制，该机制利用程序检测来为商品现成的智能设备启用完整的来源中介，以及一个网络中介点，该中介点监视设备间通信，以便从设备系统中提取网络来源。最后，该项目正在开发算法和协议，以安全地提取和聚合设备来源到集中存储库，从而实现基于来源的物联网众包监控。这项工作不仅将为智能设备功能的信任奠定基础，而且还将使基于来源的分析的进一步研究成为可能。

Computers are increasingly pervasive and diverse, embedded in devices ranging from smart phones and wearable computers to home automation devices and automotive systems. This explosive growth has far outpaced the speed with which device behaviors can be analyzed and understood, creating unprecedented opportunities for "Internet of Things" devices to engage in nefarious activities such as violating users' privacy or spreading malware. This project is designing and implementing new frameworks that track the provenance (i.e., history) of data processing and communications in systems of smart devices. To facilitate the identification of malicious behaviors, the project is developing non-invasive techniques for extracting device provenance, and presenting a public accountability infrastructure through which the history of interactions between smart devices can be analyzed.In light of the great diversity of computing platforms in this environment, the efficient extraction of fine-grained data provenance is difficult. To overcome these challenges, the researchers are designing and implementing minimally-invasive mechanisms and associated algorithms for the observation of smart device activity at multiple system layers. The expected results include the development of a retrofit mechanism that leverages program instrumentation to enable complete provenance mediation for commodity-off-the-shelf smart devices, and a network mediation point that monitors inter-device communication in order to extract network provenance from systems of devices. Finally, the project is developing algorithms and protocols to securely extract and aggregate device provenance to a centralized repository, enabling provenance-based crowd-sourced monitoring of the Internet of Things. This work will not only establish foundations for trust in the functionality of smart devices, but also enable further research in provenance-based analytics.