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  • 1.
    Gehrmann, C.
    et al.
    Lund University, Sweden.
    Gunnarsson, Martin
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Lund University, Sweden.
    A digital twin based industrial automation and control system security architecture2020In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 1, p. 669-680, article id 8822494Article in journal (Refereed)
    Abstract [en]

    The digital twin is a rather new industrial control and automation systems concept. While the approach so far has gained interest mainly due to capabilities to make advanced simulations and optimizations, recently the possibilities for enhanced security have got attention within the research community. In this article, we discuss how a digital twin replication model and corresponding security architecture can be used to allow data sharing and control of security-critical processes. We identify design-driving security requirements for digital twin based data sharing and control. We show that the proposed state synchronization design meets the expected digital twin synchronization requirements and give a high-level design and evaluation of other security components of the architecture. We also make performance evaluations of a proof of concept for protected software upgrade using the proposed digital twin design. Our new security framework provides a foundation for future research work in this promising new area.

  • 2.
    Höglund, Joel
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Lindemer, Samuel
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Furuhed, Martin
    Technology Nexus Secured Business Solutions, Sweden.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    PKI4IoT: Towards public key infrastructure for the Internet of Things2020In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 89, article id 101658Article in journal (Refereed)
    Abstract [en]

    Public Key Infrastructure is the state-of-the-art credential management solution on the Internet. However, the millions of constrained devices that make of the Internet of Things currently lack a centralized, scalable system for managing keys and identities. Modern PKI is built on a set of protocols which were not designed for constrained environments, and as a result many small, battery-powered IoT devices lack the required computing resources. In this paper, we develop an automated certificate enrollment protocol light enough for highly constrained devices, which provides end-to-end security between certificate authorities (CA) and the recipient IoT devices. We also design a lightweight profile for X.509 digital certificates with CBOR encoding, called XIOT. Existing CAs can now issue traditional X.509 to IoT devices. These are converted to and from the XIOT format by edge devices on constrained networks. This procedure preserves the integrity of the original CA signature, so the edge device performing certificate conversion need not be trusted. We implement these protocols within the Contiki embedded operating system and evaluate their performance on an ARM Cortex-M3 platform. Our evaluation demonstrates reductions in energy expenditure and communication latency. The RAM and ROM required to implement these protocols are on par with the other lightweight protocols in Contiki’s network stack.

  • 3.
    Köckemann, Uwe
    et al.
    Örebro University, Sweden.
    Alirezaie, Marjan
    Örebro University, Sweden.
    Renoux, Jennifer
    Örebro University, Sweden.
    Tsiftes, Nicolas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Uddin Ahmed, Mobyen
    Mälardalen University, Sweden.
    Morberg, Daniel
    Mälardalen University, Sweden.
    Lindén, Maria
    Mälardalen University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes2020In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 3, article id 879Article in journal (Refereed)
    Abstract [en]

    As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods.While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning.It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

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