| Title | Prov-IoT: A Security-Aware IoT Provenance Model |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Jaigirdar, Fariha Tasmin, Rudolph, Carsten, Bain, Chris |
| Conference Name | 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) |
| Date Published | Dec. 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-0392-4 |
| Keywords | composability, Computational modeling, data propagation, Documentation, Human Behavior, Internet of Things, IoT provenance model, IoT security, IoT-Health scenario, metadata, Metrics, privacy, Provenance, provenance graph, provenance-based security, pubcrawl, resilience, Resiliency, risk management, security, security metadata |
| Abstract | A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of a large amount of data collected from numerous sources. However, the highly dynamic nature of IoT network prevents the establishment of clear security perimeters and hampers the understanding of security aspects. Risk assessment in such networks requires good situational awareness with respect to security. Therefore, a comprehensive view of data propagation including information on security controls can improve security analysis and risk assessment in each layer of data propagation in an IoT architecture. Documentation of metadata is already used in data provenance to identify who generates which data, how, and when. However, documentation of security information is not seen as relevant for data provenance graphs. In this paper, we discuss the importance of adding security metadata in a data provenance graph. We propose a novel IoT Provenance model, Prov-IoT, which documents the history of data records considering data processing and aggregation along with security metadata to enable a foundation for trust in data. The model portrays a comprehensive framework and outlines the identification of information to be included in designing a security-aware provenance graph. This can be beneficial for uncovering system fault or intrusion. Also, it can be useful for decision-based systems for security analysis and risk estimation. We design an associated class diagram for the Prov-IoT model. Finally, we use an IoT healthcare example scenario to demonstrate the impact of the proposed model. |
| URL | https://ieeexplore.ieee.org/document/9343199 |
| DOI | 10.1109/TrustCom50675.2020.00183 |
| Citation Key | jaigirdar_prov-iot_2020 |
Prov-IoT: A Security-Aware IoT Provenance Model