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Hardware Trojan Detection using Transition Probability with Minimal Test Vectors

Published:29 October 2022Publication History
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Abstract

Hardware Trojans (HTs) are malicious manipulations of the standard functionality of an integrated circuit (IC). Sophisticated defense against HT attacks has become the utmost current research endeavor. In particular, the HTs whose operations depend on the rare activation condition are the most critical ones. Among other techniques, logic test by rare net excitation is advocated as one of the viable detection methods due to no extra hardware requirement. However, logic test faces a tremendous challenge of the overhead of testing configuration. This work presents a methodology based on the primary input’s impact over rare nets using transition probability to select the useful test vectors. To generate a test vector, each input’s toggle probability is calculated, which drastically minimizes the search space. The capability of rare-signal generation selects the final list of test vectors. Simulations performed in the presence of different HT triggers on different benchmark circuits, like ISCAS ’85, ISCAS ’89, and ITC ’99, show that the proposed methodology is capable of producing test vectors with significantly improved rare net coverage. Furthermore, compared to an existing technique, the proposed methodology produces average higher rare switching (around 72%) inside a netlist.

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      • Published in

        cover image ACM Transactions on Embedded Computing Systems
        ACM Transactions on Embedded Computing Systems  Volume 22, Issue 1
        January 2023
        512 pages
        ISSN:1539-9087
        EISSN:1558-3465
        DOI:10.1145/3567467
        • Editor:
        • Tulika Mitra
        Issue’s Table of Contents

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        New York, NY, United States

        Publication History

        • Published: 29 October 2022
        • Online AM: 8 August 2022
        • Accepted: 24 May 2022
        • Revised: 28 April 2022
        • Received: 16 July 2021
        Published in tecs Volume 22, Issue 1

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