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Hardware Trust and Assurance through Reverse Engineering: A Tutorial and Outlook from Image Analysis and Machine Learning Perspectives

Authors:

Ulbert J. Botero,

Mir Tanjidur Rahman,

Mukhil A. Mallaiyan,

Navid Asadizanjani,

Mark M. Tehranipoor,

Damon L. Woodard,

Domenic ForteAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 17, Issue 4

Article No.: 62, Pages 1 - 53

https://doi.org/10.1145/3464959

Published: 30 June 2021 Publication History

All formats PDF

Abstract

In the context of hardware trust and assurance, reverse engineering has been often considered as an illegal action. Generally speaking, reverse engineering aims to retrieve information from a product, i.e., integrated circuits (ICs) and printed circuit boards (PCBs) in hardware security-related scenarios, in the hope of understanding the functionality of the device and determining its constituent components. Hence, it can raise serious issues concerning Intellectual Property (IP) infringement, the (in)effectiveness of security-related measures, and even new opportunities for injecting hardware Trojans. Ironically, reverse engineering can enable IP owners to verify and validate the design. Nevertheless, this cannot be achieved without overcoming numerous obstacles that limit successful outcomes of the reverse engineering process. This article surveys these challenges from two complementary perspectives: image processing and machine learning. These two fields of study form a firm basis for the enhancement of efficiency and accuracy of reverse engineering processes for both PCBs and ICs. In summary, therefore, this article presents a roadmap indicating clearly the actions to be taken to fulfill hardware trust and assurance objectives.

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Index Terms

Hardware Trust and Assurance through Reverse Engineering: A Tutorial and Outlook from Image Analysis and Machine Learning Perspectives
1. Security and privacy
  1. Security in hardware
    1. Hardware attacks and countermeasures
      1. Malicious design modifications
    2. Hardware reverse engineering

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cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 17, Issue 4

October 2021

446 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3472280

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Published: 30 June 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Received: 01 October 2020

Published in JETC Volume 17, Issue 4

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Buggineni VChen CCamelio J(2024)Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utilityFrontiers in Manufacturing Technology10.3389/fmtec.2024.13201664Online publication date: 13-Feb-2024
https://doi.org/10.3389/fmtec.2024.1320166
Zhang YHe ALi JRezine APeng ZLarsson EYang TJiang JLi H(2024)On Modeling and Detecting Trojans in Instruction SetsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.338955843:10(3226-3239)Online publication date: Oct-2024
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Tee YCheng DShi YLin TGwee B(2024)Integrated Circuit Mask–Generative Adversarial Network for Circuit Annotation With Targeted Data AugmentationIEEE Intelligent Systems10.1109/MIS.2023.330659939:1(37-45)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/MIS.2023.3306599
Marazzi MSachsenweger TSolt FZeng PTakashi KYarema MRazavi K(2024)HiFi-DRAM: Enabling High-fidelity DRAM Research by Uncovering Sense Amplifiers with IC Imaging2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)10.1109/ISCA59077.2024.00020(133-149)Online publication date: 29-Jun-2024
https://doi.org/10.1109/ISCA59077.2024.00020
Safa MSchaumont PTajik S(2024)Parasitic Circus: On the Feasibility of Golden-Free PCB Verification2024 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)10.1109/IPFA61654.2024.10691201(01-07)Online publication date: 15-Jul-2024
https://doi.org/10.1109/IPFA61654.2024.10691201
Tee YHong XCheng DLin TShi YGwee B(2024)Unsupervised Domain Adaptation with Pseudo Shape Supervision for IC Image Segmentation2024 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA)10.1109/IPFA61654.2024.10690992(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/IPFA61654.2024.10690992
Yang ZLiang KShi ZHan QZhao WZeng Z(2024)Circuit Object Detection and Standard Cell Recognition in IC Images Based on Semantic Segmentation Techniques2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA61593.2024.10709193(300-303)Online publication date: 28-Jun-2024
https://doi.org/10.1109/ICIPCA61593.2024.10709193
Nyako KDhakal ULi FBorra V(2024)Trusted microelectronics: reverse engineering chip die using U-Net convolutional networkEngineering Research Express10.1088/2631-8695/ad7c066:4(045311)Online publication date: 9-Oct-2024
https://doi.org/10.1088/2631-8695/ad7c06
Liu CWang KLi QZhao FZhao KMa H(2024)Novel methods for locating and matching IC cells based on standard cell librariesMicroelectronic Engineering10.1016/j.mee.2023.112107283:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.mee.2023.112107
Bhatta NSingh HGhimire AAmsaad F(2024)Analyzing Aging Effects on SRAM PUFs: Implications for Security and ReliabilityJournal of Hardware and Systems Security10.1007/s41635-024-00154-68:3(174-186)Online publication date: 27-Jul-2024
https://doi.org/10.1007/s41635-024-00154-6
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