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On using control signals for word-level identification in a gate-level netlist

Published: 07 June 2015 Publication History

Abstract

This work tackles the problem of reverse engineering a gate-level netlist in order to identify groups of wires corresponding to words. It serves as the major step to find high-level modules and analyze their correct functionality in the presence of Hardware Trojans. Our core idea is to find and utilize control signals to more effectively identify words. Specifically, modern designs provide ample opportunities because they contain numerous control signals which are automatically inserted by the CAD tools. But finding control signals is itself an unresolved challenge. We propose a procedure to identify words which at its core finds and utilizes a small subset of relevant control signals by exploiting partial structural similarity. In our experiments, we show the effectiveness of our procedure by showing a high number of identified words with high accuracy using many benchmarks with already-identified words as the reference case.

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Cited By

View all
  • (2021)Hardware Trojan Classification at Gate-level Netlists based on Area and Power Machine Learning Analysis2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI51109.2021.00081(412-417)Online publication date: Jul-2021
  • (2019)NETAProceedings of the 24th Asia and South Pacific Design Automation Conference10.1145/3287624.3288739(90-95)Online publication date: 21-Jan-2019
  • (2018)The Old Frontier of Reverse Engineering: Netlist PartitioningJournal of Hardware and Systems Security10.1007/s41635-018-0043-42:3(201-213)Online publication date: 10-Sep-2018
  • Show More Cited By

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  1. On using control signals for word-level identification in a gate-level netlist

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      cover image ACM Conferences
      DAC '15: Proceedings of the 52nd Annual Design Automation Conference
      June 2015
      1204 pages
      ISBN:9781450335201
      DOI:10.1145/2744769
      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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      Published: 07 June 2015

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      Author Tags

      1. control signal identification
      2. reverse engineering
      3. structural matching

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      DAC '15: The 52nd Annual Design Automation Conference 2015
      June 7 - 11, 2015
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      View all
      • (2021)Hardware Trojan Classification at Gate-level Netlists based on Area and Power Machine Learning Analysis2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI51109.2021.00081(412-417)Online publication date: Jul-2021
      • (2019)NETAProceedings of the 24th Asia and South Pacific Design Automation Conference10.1145/3287624.3288739(90-95)Online publication date: 21-Jan-2019
      • (2018)The Old Frontier of Reverse Engineering: Netlist PartitioningJournal of Hardware and Systems Security10.1007/s41635-018-0043-42:3(201-213)Online publication date: 10-Sep-2018
      • (2016)In-situ Trojan authentication for invalidating hardware-Trojan functions2016 17th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED.2016.7479192(152-157)Online publication date: Mar-2016
      • (2016)Hardware Trojans classification for gate-level netlists based on machine learning2016 IEEE 22nd International Symposium on On-Line Testing and Robust System Design (IOLTS)10.1109/IOLTS.2016.7604700(203-206)Online publication date: Jul-2016

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