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The Metric Matters: The Art of Measuring Trust in Electronics

Published:02 June 2019Publication History

ABSTRACT

Electronic hardware trust is an emerging concern for all stakeholders in the semiconductor industry. Trust issues in electronic hardware span all stages of its life cycle - from creation of intellectual property (IP) blocks to manufacturing, test and deployment of hardware components and all abstraction levels - from chips to printed circuit boards (PCBs) to systems. The trust issues originate from a horizontal business model that promotes reliance of third-party untrusted facilities, tools, and IPs in the hardware life cycle. Today, designers are tasked with verifying the integrity of third-party IPs before incorporating them into system-on-chip (SoC) designs. Existing trust metric frameworks have limited applicability since they are not comprehensive. They capture only a subset of vulnerabilities such as potential vulnerabilities introduced through design mistakes and CAD tools, or quantify features in a design that target a particular Trojan model. Therefore, current practice uses ad-hoc security analysis of IP cores. In this paper, we propose a vector-based comprehensive coverage metric that quantifies the overall trust of an IP considering both vulnerabilities and direct malicious modifications. We use a variable weighted sum of a design's functional coverage, structural coverage, and asset coverage to assess an IP's integrity. Designers can also effectively use our trust metric to compare the relative trustworthiness of functionally equivalent third-party IPs. To demonstrate the applicability and usefulness of the proposed metric, we utilize our trust metric on Trojan-free and Trojan-inserted variants of an IP. Our results demonstrate that we are able to successfully distinguish between trusted and untrusted IPs.

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  1. The Metric Matters: The Art of Measuring Trust in Electronics

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    • Published in

      cover image ACM Conferences
      DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019
      June 2019
      1378 pages
      ISBN:9781450367257
      DOI:10.1145/3316781

      Copyright © 2019 ACM

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      Publication History

      • Published: 2 June 2019

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