Skip to main content
SpringerLink
Log in

Machine Learning–Based Hardware Trojans Detection in Integrated Circuits: A Systematic Review

  • Conference paper
  • First Online:
Data Science and Applications (ICDSA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 818))

Included in the following conference series:

  • 74 Accesses

Abstract

A purposefully inserted additional circuit known as the Hardware Trojan (HT) is implanted inside original integrated circuits during the designing or manufacturing stages. It has the potential to manipulate circuit performance or acquire underlying information. Due to machine learning’s (ML) exceptional results across a range of learning domains, the academic and business community are now looking at how Hardware Trojan (HT) attacks can be strengthened by employing conventional methods. Only a few survey studies have thoroughly evaluated the achievements and covered the unresolved issues in this subject. The literature for methods of defining HT concerns centered on machine learning is being reviewed in this research. Specifically, we first classify all known HT attacks and later analyze the evolution of the latest machine learning models in five separate areas of HT detection: reverse engineering, side-channel analysis, and golden model-free analysis, circuit feature analysis and classification approaches. Based on the review, we analyze the lessons learned and obstacles that have emerged from prior investigations. HT Defense Studies discusses the pros and cons of Supervised and unsupervised ML. Finally, a comparison of machine learning-based and nonmachine learning–based HT detection approaches is shown and current challenges with future work are also suggested.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huang, Z., Wang, Q., Chen, Y., & Jiang, X. (2020). A survey on machine learning against hardware trojan attacks: recent advances and challenges. IEEE Access, 8, 10796–10826.

    Article  Google Scholar 

  2. Anderson, M. S., North, C. J. G., & Yiu, K. K. (2008). Towards countering the rise of the silicon trojan.

    Google Scholar 

  3. Jin, Y., & Makris, Y. (2008). Hardware trojan detection using path delay fingerprint. In 2008 IEEE International Work Hardware-Oriented Secure Trust HOST (pp. 51–57)

    Google Scholar 

  4. Banga, M., & Hsiao, M. S. (2009). A novel sustained vector technique for the detection of hardware Trojans. In Proceedings 22nd International Conference VLSI Design—Held Jointly with 7th International Conference Embedded System (pp. 327–332)

    Google Scholar 

  5. Potkonjak, M., Nahapetian, A., Nelson, M., & Massey, T. (2009). Hardware trojan horse detection using gate-level characterization. In Proceedings—Design Automation Conference (pp. 688–693)

    Google Scholar 

  6. Yin, L., Fang, B., Guo, Y., Sun, Z., Tian, Z. (2020).Hierarchically defining Internet of Things security: From CIA to CACA. International Journal Distribution Sensor Networks, 16(1)

    Google Scholar 

  7. Gubbi, K. I. et al. (2023). Hardware trojan detection using machine learning: a tutorial. ACM Transaction Embedded Computer System 22(3)

    Google Scholar 

  8. Sharma, R., & Ranjan, P. (2021). A review: machine learning based hardware trojan detection. In IEMECON 2021—10th International Conference Internet Everything, Microwave Engineering Communication Networks (pp. 1–4)

    Google Scholar 

  9. Elnaggar, R., & Chakrabarty, K. (2018). Machine learning for hardware security: opportunities and risks. Journal Electronics Testing Theory Application, 34(2), 183–201.

    Article  Google Scholar 

  10. Farhat, N. H. (1992). Photonit neural networks and learning mathines the role of electron-trapping materials. IEEE Expert System Their Application, 7(5), 63–72.

    Google Scholar 

  11. Society, R. S., Society,R. S., & Statistics, A. (2012). Algorithm AS 136 A K-means clustering algorithm 28(1), 100–108

    Google Scholar 

  12. Liu, Y., Member, S., Jin, Y., Nosratinia, A. (2016). Silicon demonstration of hardware trojan design and detection in wireless cryptographic ICs. pp. 1–14.

    Google Scholar 

  13. Mcculloch, W. S., & Pitts, W. (1990). A logical calculus nervous activity. Bulletin Mathematical Biology, 52(1), 99–115.

    Google Scholar 

  14. Chen, X., Wang, L., Wang, Y., Liu, Y., & Yang, H. (2017). A general framework for hardware trojan detection in digital circuits by statistical learning algorithms. IEEE Transaction Computer Design Integration Circuits System, 36(10), 1633–1646.

    Article  Google Scholar 

  15. GhasemiGol, M., Monsefi, R., Yazdi, H. S. (2009). Ellipse support vector data description. Communication Computer Information Science, 43 CCIS, 257–268

    Google Scholar 

  16. Theobald, O. (2017). Machine learning for absolute beginners, 2nd Edition-Oliver Theobald, p. 302, 1385

    Google Scholar 

  17. Lodhi, F. K., Hasan, S. R., Hasan, O., Awwadl, F. (2017). Power profiling of microcontroller’s instruction set for runtime hardware Trojans detection without golden circuit models. In Proceedings 2017 Design Automation Test Europe DATE 2017 (pp. 294–297).

    Google Scholar 

  18. Lodhi, F. K., Abbasi, I., Khalid, F., Hasan, O., Awwad, F., Hasan, S. R. (2016). A self-learning framework to detect the intruded integrated circuits. In Proceedings—IEEE International Symposium Circuits System (vol. 2016-July, pp. 1702–1705).

    Google Scholar 

  19. Jyothi, R. B., & Priya, P. K. (2021). Accuracy analysis using machine learning classifiers for hardware trojan detection. 9(7), 646–652

    Google Scholar 

  20. Karimian, N., Tehranipoor, F., Rahman, T., Kelly, S., & Forte, D. (2015). With ring oscillator network (RON)

    Google Scholar 

  21. Bao, C., Forte, D., & Srivastava, A. (2016). On reverse engineering-based hardware trojan detection. IEEE Transaction Computer Design Integration Circuits System, 35(1), 49–57.

    Article  Google Scholar 

  22. Bao, C. (2014). On application of one-class SVM to reverse engineering-based hardware trojan detection

    Google Scholar 

  23. Berkeley, U. C. & Wasson, Z. (2012). Reverse engineering circuits using behavioral pattern mining, pp. 83–88

    Google Scholar 

  24. Netlist, O. G. & Salmani, H. (2017). COTD : reference-free hardware trojan detection and recovery based on controllability and. 12(2), 338–351

    Google Scholar 

  25. Hardware trojan detection for gate-level ICs using signal correlation based clustering (pp. 471–476) 2015.

    Google Scholar 

  26. Hasegawa, K., Yanagisawa, M., Togawa, N. (2017). Hardware trojans classification for gate-level netlists using multi-layer neural networks (pp. 227–232).

    Google Scholar 

  27. Zhou, E. R. Li, S. Q., Chen, J. H., Ni, L., Zhao, Z. X., Li, J. (2017). A novel detection method for hardware trojan in third party IP cores. In Proceedings—2016 International Conference on Information System and Artificial Intelligence ISAI 2016 (pp. 528–532).

    Google Scholar 

  28. Hasegawa, K., Yanagisawa, M., Togawa, N. (2018). A hardware-trojan classification method utilizing boundary net structures, pp. 1–4.

    Google Scholar 

  29. Hoque, T., Cruz, J., Chakraborty, P., Bhunia, S. (2018). Hardware IP trust validation : learn (the untrustworthy), and verify. In 2018 IEEE International Test Conference, pp. 1–10

    Google Scholar 

  30. Bhunia, B. S., Hsiao, M. S., Banga, M., Narasimhan, S. (2014). Hardware trojan attacks : threat analysis and countermeasures.

    Google Scholar 

  31. Brownlee, J. (2016). Supervised and unsupervised machine learning algorithms. Understand Machine Learn Algorithms, 1–9.

    Google Scholar 

  32. Tang, Y., Fang, L., Li, S. (2019). Activity factor based hardware trojan detection and localization. pp. 293–302.

    Google Scholar 

  33. Wang, S., Dong, X., Sun, K., Cui, Q., Li, D., He, C. (2016). Hardware trojan detection based on ELM neural network. no. 7, pp. 400–403.

    Google Scholar 

  34. Xue, M., Bian, R., Liu, W., & Wang, J. (2019). Defeating untrustworthy testing parties: a novel Hybrid clustering ensemble based golden models-free hardware trojan detection method. IEEE Access, 7, 5124–5140.

    Article  Google Scholar 

  35. Wang, H., Forte, D. (2023). HT-EMIS : A deep learning tool for hardware trojan detection and identification through runtime EM side-channels. Association for Computing Machinery, 1(1)

    Google Scholar 

  36. Pan, Z., Mishra, P. (2023). AI trojan attack for evading machine learning-based detection of hardware Trojans. IEEE Transactions on Computers, 3–8

    Google Scholar 

  37. Tang, W., Su, J., Gao, Y. (2023). Hardware Trojan detection method based on dual discriminator assisted conditional generation adversarial network. Journal Electronic Testing. Theory Application 0123456789

    Google Scholar 

  38. Jap, D., He, W., Bhasin, S. (2016). Supervised and unsupervised machine learning for side-channel based Trojan detection. In proceedings international conference on application systems, architectures processors (vol. 2016-Novem, pp. 17–24).

    Google Scholar 

  39. Salmani, H. (2017). COTD: reference-free hardware trojan detection and recovery based on controllability and observability in gate-level netlist. IEEE Transactions on Information Forensics and Security, 12(2), 338–350.

    Article  Google Scholar 

  40. Xue, M., Wang, J., Hux, A. (2017). An enhanced classification-based golden chips-free hardware Trojan detection technique. In Proceeding 2016 IEEE Asian Hardware Oriented Security and Trust Sympose AsianHOST 2016.

    Google Scholar 

  41. Chockaiah, N. S., Kayal, S. K. S., Malar, J. K., Kirithika, P., & Devi, M. N. (2021). Hardware trojan detection using machine learning technique. Advances in Intelligent Systems and Computing, 1245, 415–423.

    Article  Google Scholar 

  42. Jin, Y., Maliuk, D., Makris, Y. (2012). Post-deployment trust evaluation in wireless cryptographic ICs. In Proceedings-Design, Automation and Test in Europe DATE (pp. 965–970).

    Google Scholar 

  43. Iwase, T., Nozaki, Y., Yoshikawa, M., & Kumaki, T. (2016). Detection technique for hardware Trojans using machine learning in frequency domain. 2015 IEEE 4th Globle Conference on Consumer Electroniccs GCCE, 2015, 185–186.

    Google Scholar 

  44. Noor, N. Q. M., Sjarif, N. N. A., Azmi, N. H. F. M., Daud, S. M., Kamardin, K. (2017). Hardware trojan identification using machine learning-based classification. Journal of Telecommunication, Electronic and Computer Engineering, 9(3–4), 23–27.

    Google Scholar 

  45. Liakos, K. G., Georgakilas, G. K., Moustakidis, S., Sklavos, N., Plessas, F. C. (2020). Conventional and machine learning approaches as countermeasures against hardware trojan attacks. Microprocessors and Microsysems, 79(10), 103295.

    Google Scholar 

  46. Hospodar, G., Gierlichs, B., De Mulder, E., Verbauwhede, I., & Vandewalle, J. (2011). Machine learning in side-channel analysis: A first study. Journal of Cryptographic Engineering, 1(4), 293–302.

    Article  Google Scholar 

  47. Cui, Q., Sun, K., Wang, S., Zhang, L., Li, D. (2016). Hardware Trojan detection based on cluster analysis of Mahalanobis distance. In Proceedings 2016 8th international conference intelligent human-machine systems and cybernetics IHMSC 2016 (Vol. 1, no. 328201505, pp. 234–238).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Engineering and Management (UEM), Jaipur, India

    Ritu Sharma & Prashant Ranjan

Authors
  1. Ritu Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prashant Ranjan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ritu Sharma .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Satyasai Jagannath Nanda

  2. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajendra Prasad Yadav

  3. Department of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

  4. Department of Computer Science and Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharma, R., Ranjan, P. (2024). Machine Learning–Based Hardware Trojans Detection in Integrated Circuits: A Systematic Review. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 818. Springer, Singapore. https://doi.org/10.1007/978-981-99-7862-5_3

Download citation

Publish with us

Policies and ethics

Search

Navigation