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  • Secure and efficient software-based attestation for industrial control devices with arm processors

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    Bai213842-Accepted.pdf (1.438Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Chen, B
    Dong, X
    Bai, G
    Jauhar, S
    Cheng, Y
    Griffith University Author(s)
    Bai, Guangdong
    Year published
    2017
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    Abstract
    For industrial control systems, ensuring the software integrity of their devices is a key security requirement. A pure software-based attestation solution is highly desirable for protecting legacy field devices that lack hardware root of trust (e.g., Trusted Platform Module). However, for the large population of field devices with ARM processors, existing software-based attestation schemes either incur long attestation time or are insecure. In this paper, we design a novel memory stride technique that significantly reduces the attestation time while remaining secure against known attacks and their advanced variants on ARM ...
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    For industrial control systems, ensuring the software integrity of their devices is a key security requirement. A pure software-based attestation solution is highly desirable for protecting legacy field devices that lack hardware root of trust (e.g., Trusted Platform Module). However, for the large population of field devices with ARM processors, existing software-based attestation schemes either incur long attestation time or are insecure. In this paper, we design a novel memory stride technique that significantly reduces the attestation time while remaining secure against known attacks and their advanced variants on ARM platform. We analyze the scheme's security and performance based on the formal framework proposed by Armknecht et al. [7] (with a necessary change to ensure its applicability in practical settings). We also implement memory stride on two models of real-world power grid devices that are widely deployed today, and demonstrate its superior performance.
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    Conference Title
    ACSAC 2017: Proceedings of the 33rd Annual Computer Security Applications Conference
    DOI
    https://doi.org/10.1145/3134600.3134621
    Copyright Statement
    © ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACSAC 2017: Proceedings of the 33rd Annual Computer Security Applications Conference, ISBN: 978-1-4503-5345-8, https://doi.org/10.1145/3134600.3134621
    Subject
    Artificial intelligence
    Software engineering
    Publication URI
    http://hdl.handle.net/10072/411916
    Collection
    • Conference outputs

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