research-article

Golden Chip-Free Trojan Detection Leveraging Trojan Trigger’s Side-Channel Fingerprinting

Authors:

Yiqiang ZhaoAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 20, Issue 1

Article No.: 6, Pages 1 - 18

https://doi.org/10.1145/3419105

Published: 07 December 2020 Publication History

Abstract

Hardware Trojans (HTs) have become a major threat for the integrated circuit industry and supply chain and have motivated numerous developments of HT detection schemes. Although the side-channel HT detection approach is among the most promising solutions, most of the previous methods require a trusted golden chip reference. Furthermore, detection accuracy is often influenced by environmental noise and process variations. In this article, a novel electromagnetic (EM) side-channel fingerprinting-based HT detection method is proposed. Different from previous methods, the proposed solution eliminates the requirement of a trusted golden fabricated chip. Rather, only the genuine RTL code is required to generate the EM signatures as references. A factor analysis method is utilized to extract the spectral features of the HT trigger’s EM radiation, and then a k-means clustering method is applied for HT detection. Experimentation on two selected sets of Trust-Hub benchmarks has been performed on FPGA platforms, and the results show that the proposed framework can detect all dormant HTs with a high confidence level.

References

[1]

J. Aarestad, D. Acharyya, R. Rad, and J. Plusquellic. 2010. Detecting Trojans through leakage current analysis using multiple supply pad s. IEEE Transactions on Information Forensics and Security 5, 4 (Dec. 2010), 893--904.

Digital Library

[2]

Atieh Amelian and Shahram Etemadi Borujeni. 2018. A side-channel analysis for hardware Trojan detection based on path delay measurement. Journal of Circuits Systems and Computers 27, 9 (Aug. 2018), 1850138.

[3]

J. Balasch, B. Gierlichs, and I. Verbauwhede. 2015. Electromagnetic circuit fingerprints for hardware Trojan detection. In Proceedings of the 2015 IEEE International Symposium on Electromagnetic Compatibility (EMC’15). 246--251.

[4]

C. Bao, D. Forte, and A. Srivastava. 2014. On application of one-class SVM to reverse engineering-based hardware Trojan detection. In Proceedings of the 15th International Symposium on Quality Electronic Design. 47--54.

[5]

C. Bao, D. Forte, and A. Srivastava. 2015. Temperature tracking: Toward robust run-time detection of hardware Trojans. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 34, 10 (Oct. 2015), 1577--1585.

Digital Library

[6]

Barry R. Masters, Rafael C. Gonzalez, and Richard Woods. 2009. Book Review: Digital Image Processing, Third Edition. Journal of Biomedical Optics 14, 2 (2009), 029901.

[7]

Eric Brier, Christophe Clavier, and Francis Olivier. 2004. Correlation power analysis with a leakage model. In Proceedings of the International Workshop on Cryptographic Hardware and Embedded Systems. 16--29.

[8]

B. Cha and S. K. Gupta. 2012. Efficient Trojan detection via calibration of process variations. In Proceedings of the 2012 IEEE 21st Asian Test Symposium. 355--361.

Digital Library

[9]

X. Chen, L. Wang, Y. Wang, Y. Liu, and H. Yang. 2017. A general framework for hardware Trojan detection in digital circuits by statistical learning algorithms. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 36, 10 (Oct. 2017), 1633--1646.

Digital Library

[10]

Z. Chen, S. Guo, J. Wang, Y. Li, and Z. Lu. 2019. Toward FPGA security in IoT: A new detection technique for hardware Trojans. IEEE Internet of Things Journal 6, 4 (2019), 7061--7068.

[11]

F. N. Esirci and A. A. Bayrakci. 2017. Hardware Trojan detection based on correlated path delays in defiance of variations with spatial correlations. In Proceedings of the Design, Automation, and Test in Europe Conference and Exhibition (DATE’17) 163--168.

[12]

K. Hasegawa, M. Yanagisawa, and N. Togawa. 2017. Hardware Trojans classification for gate-level netlists using multi-layer neural networks. In Proceedings of the 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design (IOLTS’17). 227--232.

[13]

Jiaji He, Yanjiang Liu, Yidong Yuan, Kai Hu, Xianzhao Xia, and Yiqiang Zhao. 2018. Golden chip free Trojan detection leveraging electromagnetic side channel fingerprinting. IEICE Electronics Express 16, 2 (2018), 20181065.

[14]

Jiaji He, Haocheng Ma, Xiaolong Guo, Yiqiang Zhao, and Yier Jin. 2020. Design for EM side-channel security through quantitative assessment of RTL implementations. In Proceedings of the 2020 25th Asia and South Pacific Design Automation Conference (ASP-DAC’20). IEEE, Los Alamitos, CA, 62--67.

Digital Library

[15]

J. He, Y. Zhao, X. Guo, and Y. Jin. 2017. Hardware Trojan detection through chip-free electromagnetic side-channel statistical analysis. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 25, 10 (Oct. 2017), 2939--2948.

Digital Library

[16]

B. Hou, C. He, L. Wang, Y. En, and S. Xie. 2014. Hardware Trojan detection via current measurement: A method immune to process variation effects. In Proceedings of the International Conference on Reliability, Maintainability, and Safety (ICRMS’14). 1039--1042.

[17]

Y. Huang, S. Bhunia, and P. Mishra. 2018. Scalable test generation for Trojan detection using side channel analysis. IEEE Transactions on Information Forensics and Security 13, 11 (Nov. 2018), 2746--2760.

[18]

D. Jap, Wei He, and S. Bhasin. 2016. Supervised and unsupervised machine learning for side-channel based Trojan detection. In Proceedings of the 2016 IEEE 27th International Conference on Application-Specific Systems, Architectures, and Processors (ASAP’16). 17--24.

[19]

Yier Jin. 2015. Introduction to hardware security. Electronics 4, 4 (2015), 763--784.

[20]

A. Kulkarni, Y. Pino, and T. Mohsenin. 2016. Adaptive real-time Trojan detection framework through machine learning. In Proceedings of the 2016 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST’16). IEEE, Los Alamitos, CA, 120--123.

[21]

M. Lecomte, J. Fournier, and P. Maurine. 2017. An on-chip technique to detect hardware Trojans and assist counterfeit identification. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 25, 12 (Dec. 2017), 3317--3330.

[22]

Jun Li, Lin Ni, Jihua Chen, and E. Zhou. 2016. A novel hardware Trojan detection based on BP neural network. In Proceedings of the 2016 2nd IEEE International Conference on Computer and Communications (ICCC’16). 2790--2794.

[23]

Hongfu Liu, Junjie Wu, Tongliang Liu, Dacheng Tao, and Yun Fu. 2017. Spectral ensemble clustering via weighted k-means: Theoretical and practical evidence. IEEE Transactions on Knowledge 8 Data Engineering 29, 5 (2017), 1129--1143.

Digital Library

[24]

Yu Liu, Ke Huang, and Yiorgos Makris. 2014. Hardware Trojan detection through golden chip-free statistical side-channel fingerprinting. In Proceedings of the 51st Annual Design Automation Conference (DAC’14). Article 155, 6 pages.

Digital Library

[25]

Patrick Lysaght, Brandon Blodget, Jeff Mason, Jay Young, and Brendan Bridgford. 2006. Enhanced architectures, design methodologies and CAD tools for dynamic reconfiguration of Xilinx FPGAs. In Proceedings of the 2006 International Conference on Field Programmable Logic and Applications. IEEE, Los Alamitos, CA, 1--6.

[26]

Abhranil Maiti, Jeff Casarona, Luke McHale, and Patrick Schaumont. 2010. A large scale characterization of RO-PUF. In Proceedings of the 2010 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST’10). IEEE, Los Alamitos, CA, 94--99.

[27]

F. Menichelli, R. Menicocci, M. Olivieri, and A. Trifiletti. 2008. High-level side-channel attack modeling and simulation for security-critical systems on chips. IEEE Transactions on Dependable and Secure Computing 5, 3 (July 2008), 164--176.

Digital Library

[28]

S. Moein, J. Subramnian, T. A. Gulliver, F. Gebali, and M. W. El-Kharashi. 2015. Classification of hardware Trojan detection techniques. In Proceedings of the 2015 10th International Conference on Computer Engineering Systems (ICCES’15). 357--362.

[29]

S. Narasimhan, X. Wang, D. Du, R. S. Chakraborty, and S. Bhunia. 2011. TeSR: A robust Temporal Self-Referencing approach for hardware Trojan detection. In Proceedings of the IEEE International Symposium on Hardware-Oriented Security and Trust (HOST’11). Los Alamitos, CA, 71--74.

[30]

J. Park and I. W. Sandberg. 2014. Universal approximation using radial-basis-function networks. Neural Computation 3, 2 (2014), 246--257.

[31]

Youngok Pino, Vinayaka Jyothi, and Matthew French. 2014. Intra-die process variation aware anomaly detection in FPGAs. In Proceedings of the 2014 International Test Conference. IEEE, Los Alamitos, CA, 1--6.

[32]

R. Rad, J. Plusquellic, and M. Tehranipoor. 2008. Sensitivity analysis to hardware Trojans using power supply transient signals. In Proceedings of the 2008 IEEE International Workshop on Hardware-Oriented Security and Trust (HOST’08). Los Alamitos, CA, 3–7.

[33]

Siddika Berna Ors, Frank Grkaynak, Elisabeth Oswald, and Bart Preneel. 2004. Power-analysis attack on an ASIC AES implementation. In Proceedings of the International Conference on Information Technology: Coding and Computing. 546.

[34]

Bicky Shakya, Tony He, Hassan Salmani, Domenic Forte, Swarup Bhunia, and Mark Tehranipoor. 2017. Benchmarking of hardware Trojans and maliciously affected circuits. Journal of Hardware and Systems Security 1, 1 (March 2017), 85--102.

[35]

O. Söll, T. Korak, M. Muehlberghuber, and M. Hutter. 2014. EM-based detection of hardware Trojans on FPGAs. In Proceedings of the IEEE International Symposium on Hardware-Oriented Security and Trust (HOST’14). Los Alamitos, CA, 84--87.

[36]

F. Stellari, P. Song, A. J. Weger, J. Culp, A. Herbert, and D. Pfeiffer. 2014. Verification of untrusted chips using trusted layout and emission measurements. In Proceedings of the IEEE International Symposium on Hardware-Oriented Security and Trust (HOST’14). Los Alamitos, CA, 19--24.

[37]

M. Tehranipoor and F. Koushanfar. 2010. A survey of hardware Trojan taxonomy and detection. IEEE Design 8 Test of Computers 27, 1 (Jan. 2010), 10--25.

[38]

K. Tiri and I. Verbauwhecle. 2005. Simulation models for side-channel information leaks. In Proceedings of the 2005 42nd Design Automation Conference. 228--233.

[39]

S. Wang, X. Dong, K. Sun, Q. Cui, D. Li, and C. He. 2016. Hardware Trojan detection based on ELM neural network. In Proceedings of the 2016 1st IEEE International Conference on Computer Communication and the Internet (ICCCI’16). 400--403.

[40]

H. Xue and S. Ren. 2018. Self-reference-based hardware Trojan detection. IEEE Transactions on Semiconductor Manufacturing 31, 1 (Feb. 2018), 2--11.

[41]

M. Yoshimura, T. Bouyashiki, and T. Hosokawa. 2017. A hardware Trojan circuit detection method using activation sequence generations. In Proceedings of the 2017 IEEE 22nd Pacific Rim International Symposium on Dependable Computing (PRDC’17). 221--222.

[42]

Yang Zhang, Houde Quan, Xiongwei Li, and Kaiyan Chen. 2018. Golden-free processor hardware Trojan detection using bit power consistency analysis. Journal of Electronic Testing 34, 3 (2018), 305--312.

Digital Library

Cited By

Lee DLee JJung YKauh JSong T(2024)Robust Hardware Trojan Detection Method by Unsupervised Learning of Electromagnetic SignalsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.3458892(1-14)Online publication date: 2024
https://doi.org/10.1109/TVLSI.2024.3458892
Liao LMeng F(2024)Hardware Trojan Detection and Identification Using Electromagnetic Side-Channel Leakage2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP)10.1109/ICSP62122.2024.10743629(328-333)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICSP62122.2024.10743629
Vutukuru MEmmert JJha R(2024)A Survey of Electromagnetic Radiation Based Hardware Assurance and Reliability Monitoring Methods in Integrated CircuitsIEEE Access10.1109/ACCESS.2024.347992912(150623-150638)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3479929
Show More Cited By

Index Terms

Golden Chip-Free Trojan Detection Leveraging Trojan Trigger’s Side-Channel Fingerprinting
1. Security and privacy
  1. Security in hardware
    1. Hardware attacks and countermeasures
      1. Malicious design modifications

Recommendations

Hardware Trojan Detection Using Machine Learning: A Tutorial
With the growth and globalization of IC design and development, there is an increase in the number of Designers and Design houses. As setting up a fabrication facility may easily cost upwards of $20 billion, costs for advanced nodes may be even greater. ...
AdaTest: Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection
This paper proposes AdaTest, a novel adaptive test pattern generation framework for efficient and reliable Hardware Trojan (HT) detection. HT is a backdoor attack that tampers with the design of victim integrated circuits (ICs). AdaTest improves the ...
Test Generation for Hardware Trojan Detection Using Correlation Analysis and Genetic Algorithm
Special Issue on FDL2019

Hardware Trojan (HT) is a major threat to the security of integrated circuits (ICs). Among various HT detection approaches, side channel analysis (SCA)-based methods have been extensively studied. SCA-based methods try to detect HTs by comparing side ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 20, Issue 1

January 2021

193 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3441649

Editor:
Tulika Mitra
National University of Singapore, Singapore

Issue’s Table of Contents

Copyright © 2020 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 07 December 2020

Accepted: 01 August 2020

Revised: 01 August 2020

Received: 01 April 2020

Published in TECS Volume 20, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
266
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)1

Reflects downloads up to 02 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lee DLee JJung YKauh JSong T(2024)Robust Hardware Trojan Detection Method by Unsupervised Learning of Electromagnetic SignalsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.3458892(1-14)Online publication date: 2024
https://doi.org/10.1109/TVLSI.2024.3458892
Liao LMeng F(2024)Hardware Trojan Detection and Identification Using Electromagnetic Side-Channel Leakage2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP)10.1109/ICSP62122.2024.10743629(328-333)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICSP62122.2024.10743629
Vutukuru MEmmert JJha R(2024)A Survey of Electromagnetic Radiation Based Hardware Assurance and Reliability Monitoring Methods in Integrated CircuitsIEEE Access10.1109/ACCESS.2024.347992912(150623-150638)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3479929
Liu YLi JGuo PZhu CWang JZhong JZhang L(2024)A Feature-Adaptive and Scalable Hardware Trojan Detection Framework For Third-party IPs Utilizing Multilevel Feature Analysis and Random ForestJournal of Electronic Testing10.1007/s10836-024-06150-640:6(741-759)Online publication date: 6-Dec-2024
https://doi.org/10.1007/s10836-024-06150-6
Wang HPanoff MWang SForte DThapliyal HDeMara RPartin-Vaisband IKatkoori S(2023)HT-EMIS: A Deep Learning Tool for Hardware Trojan Detection and Identification through Runtime EM Side-ChannelsProceedings of the Great Lakes Symposium on VLSI 202310.1145/3583781.3590260(51-56)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1145/3583781.3590260
Li XHe DGao YLiu XChan SPan MChoo K(2023)LIGHT: Lightweight Authentication for Intra Embedded Integrated Electronic SystemsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.314882520:2(1088-1103)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TDSC.2022.3148825
Hasan MIslam TZubair A(2023)Efficacy of Side-Channel Analysis for Hardware Trojan Detection: A Case Study of CNTFET2023 26th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT60459.2023.10441616(1-6)Online publication date: 13-Dec-2023
https://doi.org/10.1109/ICCIT60459.2023.10441616
Krachenfels TSeifert JTajik S(2023)Trojan awakener: detecting dormant malicious hardware using laser logic state imaging (extended version)Journal of Cryptographic Engineering10.1007/s13389-023-00323-313:4(485-499)Online publication date: 29-May-2023
https://doi.org/10.1007/s13389-023-00323-3
Tang WSu JHe JGao Y(2022)A Deep Learning Method Based on the Attention Mechanism for Hardware Trojan DetectionElectronics10.3390/electronics1115240011:15(2400)Online publication date: 31-Jul-2022
https://doi.org/10.3390/electronics11152400
Ma Q(2022)Design of High-Confidence Embedded Operating System based on Artificial Intelligence and Smart Chips2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)10.1109/ICAIS53314.2022.9742917(58-62)Online publication date: 23-Feb-2022
https://doi.org/10.1109/ICAIS53314.2022.9742917
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Issue’s Table of Contents