Skip to main content

Advertisement

Springer Nature Link
Log in

A lightweight Hardware Trojan detection approach in the waveform diagram based on MobileViT and attention mechanism

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Since, the initial proposition of Hardware Trojans' potential existence in 2007, side-channel technology has been extensively employed in research concerning Hardware Trojan detection. Concurrently, the ongoing advancements in integrated circuit technology have facilitated the widespread adoption of deep learning methodologies in the Hardware Trojan detection domain. Notably, in 2020, Google’s introduction of the vision transformer ignited renewed interest in leveraging Deep Learning for hardware security. In 2021, Apple introduced the MobileViT network, which is a lightweight, versatile, and low latency network. But, it is crucial to acknowledge that as the complexity of Deep Learning model architectures deepens, there is a corresponding rise in the number of model parameters. Moreover, MobileViT faces challenges in extracting significant features and demonstrates limited sensitivity towards channel and spatial information during image feature extraction. Hence, we propose the TA-MobileViT lightweight model, which combines the almost parameterless attention mechanism—triplet attention (TA) and the lightweight universal MobileViT. Without expanding the model's parameter count, it acquires cross-channel interaction capability, improves its focus on critical information, and enhances both classification accuracy and generalization ability. By combining convolutional blocks and transformer blocks, the model has both the ability to extract local features and the ability to extract global features, thereby improving the model's expressiveness and performance, and achieving higher accuracy in detection. The experimental results show that TA-MobileViT can quickly and efficiently detect Hardware Trojans. When detecting a single Trojan, the recognition accuracy of three types reaches 100%, while the detection accuracy of AES-600 reaches the highest of 72.2%. When detecting multiple types of Hardware Trojans. Our model’s mean accuracy reached the highest of 97.04%. Compared with other deep learning methods, this network model has fewer parameters and more competitive classification accuracy. The code is available at https://github.com/GaunX/TA-MobileViT/tree/master.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

No datasets were generated or analyzed during the current study.

References

  1. Salmani H, Tehranipoor M, Karri R (2013) On design vulnerability analysis and trust benchmarks development. In: 2013 IEEE 31st International Conference on Computer Design (ICCD). IEEE, Asheville, pp 471–474

  2. Wolff F, Papachristou C, Bhunia S, Chakraborty RS (2008) Towards Trojan-free trusted ICs: problem analysis and detection scheme. In: 2008 design, automation and test in Europe. IEEE, Munich, pp 1362–1365

  3. https://www.trust-hub.org/benchmarks/trojan

  4. Courbon F, Loubet-Moundi P, Fournier JJA, Tria A (2015) A high efficiency hardware trojan detection technique based on fast SEM imaging. In: Design, Automation & Test in Europe Conference & Exhibition (DATE), 2015. IEEE Conference Publications, Grenoble, pp 788–793

  5. Ludwig M, Bette A-C, Lippmann B (2022) ViTaL: verifying Trojan-free physical layouts through hardware reverse engineering

  6. Du D, Narasimhan S, Chakraborty RS, Bhunia S (2010) Self-referencing: a scalable side-channel approach for Hardware Trojan detection. In: Mangard S, Standaert F-X (eds) Cryptographic hardware and embedded systems, CHES 2010. Springer, Berlin, pp 173–187

    Chapter  MATH  Google Scholar 

  7. Huang Y, Bhunia S, Mishra P (2018) Scalable test generation for trojan detection using side channel analysis. IEEE Trans Inf Forensic Secur 13:2746–2760. https://doi.org/10.1109/TIFS.2018.2833059

    Article  MATH  Google Scholar 

  8. Chen S, Wang T, Huang Z, Hou X (2023) Detection method of Golden Chip-Free Hardware Trojan based on the combination of ResNeXt structure and attention mechanism. Comput Secur 134:103428. https://doi.org/10.1016/j.cose.2023.103428

    Article  Google Scholar 

  9. Faezi S, Yasaei R, Al Faruque MA (2021) HTnet: transfer learning for golden chip-free Hardware Trojan detection. In: 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, Grenoble, pp 1484–1489

  10. Muralidhar N, Zubair A, Weidler N, et al (2021) Contrastive graph convolutional networks for Hardware Trojan detection in third party IP cores. In: 2021 IEEE international symposium on hardware oriented security and trust (HOST). IEEE, Tysons Corner, pp 181–191

  11. Sankaran S, Mohan VS, Purushothaman. A (2021) Deep learning based approach for hardware trojan detection. In: 2021 IEEE international symposium on smart electronic systems (iSES). IEEE, Jaipur, pp 177–182

  12. Yu S, Gu C, Liu W, O’Neill M (2022) Deep learning-based hardware trojan detection with block-based netlist information extraction. IEEE Trans Emerg Top Comput 10:1837–1853. https://doi.org/10.1109/TETC.2021.3116484

    Article  MATH  Google Scholar 

  13. Alom Z, Taha TM, Yakopcic C et al The history began from AlexNet: a comprehensive survey on deep learning approaches

  14. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition

  15. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition

  16. Agrawal D, Baktir S, Karakoyunlu D, et al (2007) Trojan detection using IC fingerprinting. In: 2007 IEEE symposium on security and privacy (SP’07). IEEE, Berkeley, pp 296–310

  17. Jin Y, Kupp N, Makris Y (2009) Experiences in Hardware Trojan design and implementation. In: 2009 IEEE international workshop on hardware-oriented security and trust. IEEE, San Francisco, pp 50–57

  18. Yier Jin, Makris Y (2008) Hardware Trojan detection using path delay fingerprint. In: 2008 IEEE international workshop on hardware-oriented security and trust. IEEE, Anaheim, pp 51–57

  19. Chakraborty RS, Wolff F, Paul S et al (2009) MERO: a statistical approach for hardware trojan detection. In: Clavier C, Gaj K (eds) Cryptographic hardware and embedded systems—CHES 2009. Springer, Berlin, pp 396–410

    Chapter  MATH  Google Scholar 

  20. Xu X, Li XW, Zhang Y, Xie FF (2013) A non-invasive hardware trojan detection approach based on side-channel analysis. AMM 401–403:1776–1780. https://doi.org/10.4028/www.scientific.net/AMM.401-403.1776

    Article  MATH  Google Scholar 

  21. Jha S, Jha SK (2008) Randomization based probabilistic approach to detect Trojan circuits. In: 2008 11th IEEE high assurance systems engineering symposium. IEEE, Nanjing, pp 117–124

  22. Rad R, Plusquellic J, Tehranipoor M (2010) A sensitivity analysis of power signal methods for detecting Hardware Trojans under real process and environmental conditions. IEEE Trans VLSI Syst 18:1735–1744. https://doi.org/10.1109/TVLSI.2009.2029117

    Article  Google Scholar 

  23. Forte D, Bao C, Srivastava A (2013) Temperature tracking: an innovative run-time approach for hardware Trojan detection. In: 2013 IEEE/ACM International Conference on COMPUTER-AIDED Design (ICCAD). IEEE, San Jose, pp 532–539

  24. Stellari F, Song P, Weger AJ, et al (2014) Verification of untrusted chips using trusted layout and emission measurements. In: 2014 IEEE international symposium on hardware-oriented security and trust (HOST). IEEE, Arlington, pp 19–24

  25. Salmani H, Plusquellic J (2012) A novel technique for improving Hardware Trojan detection and reducing Trojan activation time. IEEE Trans Very Large Scale Integr VLSI Syst 20:112–125

    Article  MATH  Google Scholar 

  26. He J, Zhao Y, Guo X, Jin Y (2017) Hardware Trojan detection through chip-free electromagnetic side-channel statistical analysis. IEEE Trans VLSI Syst 25:2939–2948. https://doi.org/10.1109/TVLSI.2017.2727985

    Article  MATH  Google Scholar 

  27. Faezi S, Yasaei R, Barua A, Faruque MAA (2021) Brain-inspired golden chip free Hardware Trojan detection. IEEE Trans Inf Forensic Secur 16:2697–2708. https://doi.org/10.1109/TIFS.2021.3062989

    Article  Google Scholar 

  28. Ghosh S, Basak A, Bhunia S (2015) How secure are printed circuit boards against Trojan attacks? IEEE Des Test 32:7–16. https://doi.org/10.1109/MDAT.2014.2347918

    Article  MATH  Google Scholar 

  29. Liu Y, Huang K, Makris Y (2014) Hardware Trojan detection through golden chip-free statistical side-channel fingerprinting. In: Proceedings of the 51st Annual Design Automation Conference. ACM, San Francisco, pp 1–6

  30. Narasimhan S, Yueh W, Wang X et al (2012) Improving IC security against trojan attacks through integration of security monitors. IEEE Des Test Comput 29:37–46. https://doi.org/10.1109/MDT.2012.2210183

    Article  MATH  Google Scholar 

  31. Xuehui Zhang, Tehranipoor M (2011) RON: an on-chip ring oscillator network for hardware Trojan detection. In: 2011 Design, automation & test in Europe. IEEE, Grenoble, pp 1–6

  32. Chakraborty RS, Bhunia S (2009) Security against hardware Trojan through a novel application of design obfuscation. In: Proceedings of the 2009 International Conference on Computer-Aided Design. ACM, San Jose, pp 113–116

  33. Hicks M, Finnicum M, King ST, et al (2010) Overcoming an untrusted computing base: detecting and removing malicious hardware automatically. In: 2010 IEEE symposium on security and privacy. IEEE, Oakland, pp 159–172

  34. Dubeuf J, Hely D, Karri R (2013) Run-time detection of hardware Trojans: the processor protection unit. In: 2013 18th IEEE European test symposium (ETS). IEEE, Avignon, pp 1–6

  35. Jin Y, Sullivan D (2014) Real-time trust evaluation in integrated circuits. In: Design, Automation & Test in Europe Conference & Exhibition (DATE), 2014. IEEE Conference Publications, Dresden, pp 1–6

  36. Bao C, Forte D, Srivastava A (2014) On application of one-class SVM to reverse engineering-based hardware Trojan detection. In: Fifteenth international symposium on quality electronic design. IEEE, Santa Clara, pp 47–54

  37. Jun Li, Lin Ni, Jihua Chen, Zhou E (2016) A novel hardware Trojan detection based on BP neural network. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, Chengdu, pp 2790–2794

  38. Hasegawa K, Oya M, Yanagisawa M, Togawa N (2016) Hardware Trojans classification for gate-level netlists based on machine learning. In: 2016 IEEE 22nd international symposium on on-line testing and robust system design (IOLTS). IEEE, Sant Feliu de Guixols, pp 203–206

  39. Liakos KG, Georgakilas GK, Plessas FC, Kitsos P (2022) GAINESIS: generative artificial intelligence netlists synthesis. Electronics 11:245. https://doi.org/10.3390/electronics11020245

    Article  Google Scholar 

  40. Han K, Wang Y, Chen H et al (2023) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45:87–110. https://doi.org/10.1109/TPAMI.2022.3152247

    Article  MATH  Google Scholar 

  41. Li Y, Li S, Shen H (2023) HTrans: transformer-based method for Hardware Trojan detection and localization. In: 2023 IEEE 32nd Asian test symposium (ATS). IEEE, Beijing, pp 1–6

  42. Mehta S, Rastegari M (2021) Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv 2021. https://doi.org/10.48550/arXiv.2110.02178

  43. Sandler M, Howard A, Zhu M et al (2019) MobileNetV2: inverted residuals and linear bottlenecks

  44. Misra D, Nalamada T, Arasanipalai AU, Hou Q (2020) Rotate to attend: convolutional triplet attention module

  45. Hu J, Shen L, Albanie S et al (2017) Squeeze-and-excitation networks. https://doi.org/10.48550/ARXIV.1709.01507

  46. Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. https://doi.org/10.48550/ARXIV.1807.06521

  47. Yasaei R (2022) Hardware Trojan power & EM SIDE-channel dataset. In: Proceedings of the IEEE DataPort. Accessed 17 Oct 2022

  48. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. https://doi.org/10.48550/ARXIV.2103.02907

  49. Huang G, Liu Z, van der Maaten L, Weinberger KQ (2018) Densely connected convolutional networks

  50. Liu Z, Mao H, Wu C-Y et al (2022) A ConvNet for the 2020s

  51. Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An image is worth 16 × 16 words: transformers for image recognition at scale

  52. Kulkarni A, Pino Y, Mohsenin T (2016) SVM-based real-time hardware Trojan detection for many-core platform. In: 2016 17th international symposium on quality electronic design (ISQED). IEEE, Santa Clara, pp 362–367

  53. Kkalais (2020) Machine learning techniques for hardware Trojan detection. In: github.com. Available: https://github.com/Kkalais/Hardware-Trojan-Detection. Accessed 1 Oct 2022.

  54. Tang W, Su J, He J, Gao Y (2022) A deep learning method based on the attention mechanism for Hardware Trojan detection. Electronics 11:2400. https://doi.org/10.3390/electronics11152400

    Article  MATH  Google Scholar 

Download references

Funding

The research work reported in this paper is supported by Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (No. YQ22102). Innovation Project of GUET Graduate Education (2024YCXS123, 2024YCXS118). Guangxi Natural Science Foundation (2025GXNSFAA069223). Guangxi Key Laboratory of Intelligent Transportation System (No. 2023JJXKJC03).

Author information

Authors and Affiliations

  1. Guangxi Key Laboratory of Intelligent Transportation System, Guilin University of Electronic Technology, Guilin, 541000, China

    Xingna Hou

  2. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541000, China

    Shouhong Chen, Guanxiang Qin, Ying Lu, Tao Wang & Xingna Hou

Authors
  1. Shouhong Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Guanxiang Qin
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ying Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Tao Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xingna Hou
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Shouhong Chen was involved in conceptualization, methodology, software, investigation, funding acquisition, supervision, and formal analysis. Guanxiang Qin was involved in data curation, validation, visualization, software, supervision, and writing—original draft. Ying Lu was involved in visualization, investigation, supervision, resources, writing—review and editing. Tao Wang was involved in writing—review and editing. Xingna Hou was involved in conceptualization, supervision, funding acquisition, resources, supervision, writing—review and editing.

Corresponding author

Correspondence to Xingna Hou.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Qin, G., Lu, Y. et al. A lightweight Hardware Trojan detection approach in the waveform diagram based on MobileViT and attention mechanism. J Supercomput 81, 580 (2025). https://doi.org/10.1007/s11227-025-07080-6

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-025-07080-6

Keywords