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Novel Feature Selection for Non-destructive Detection of Hardware Trojans Using Hyperspectral Scanning

Abstract

This paper introduces a novel method of non-destructively detecting incredibly elusive dormant hardware Trojans by selectively capturing hyperspectral backscattering measurements across the physical area of an integrated circuit. We propose a novel approach that pre-filters and actively samples an automatically selected set of the hyperspectral measurement space to significantly reduce measurement time while improving the ability to robustly detect dormant hardware Trojans. We demonstrate that our selective hyperspectral scanning approach can detect dormant hardware Trojans taking up as little as 0.03% of the circuit, which is up to 14 times smaller than prior work.

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References

  1. Bhunia S, Hsiao MS, Banga M, Narasimhan S (2014) Hardware trojan attacks: Threat analysis and countermeasures. Proc IEEE 102(8):1229–1247. https://doi.org/10.1109/JPROC.2014.2334493

    Article  Google Scholar 

  2. Tehranipoor M, Koushanfar F (2010) A survey of hardware trojan taxonomy and detection. IEEE Des Test Comput 27(1):10–25. https://doi.org/10.1109/MDT.2010.7

    Article  Google Scholar 

  3. Wang X, Tehranipoor M, Plusquellic J (2008) Detecting malicious inclusions in secure hardware: Challenges and solutions. In: 2008 IEEE International Workshop on Hardware-Oriented Security and Trust, pp 15–19. https://doi.org/10.1109/HST.2008.4559039

  4. Xiao K, Forte D, Jin Y, Karri R, Bhunia S, Tehranipoor M (2016) Hardware trojans: Lessons learned after one decade of research. ACM Trans Des Autom Electron Syst 22(1). https://doi.org/10.1145/2906147

  5. Botero UJ, Wilson R, Lu H, Rahman MT, Mallaiyan MA, Ganji F, Asadizanjani N, Tehranipoor MM, Woodard DL, Forte D (2021) Hardware trust and assurance through reverse engineering: A tutorial and outlook from image analysis and machine learning perspectives. J Emerg Technol Comput Syst 17(4)

  6. Dogan H, Forte D, Tehranipoor MM (2014) Aging analysis for recycled fpga detection. In: 2014 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), pp 171–176

  7. Kimura A, Scholl J, Schaffranek J, Sutter M, Elliott A, Strizich M, Via GD (2020) A decomposition workflow for integrated circuit verification and validation. Journal of Hardware and Systems Security 4(1):34–43

    Article  Google Scholar 

  8. Menzel E, Kubalek E (1983) Fundamentals of electron beam testing of integrated circuits. Scanning 5(3):103–122. https://doi.org/10.1002/sca.4950050301, https://onlinelibrary.wiley.com/doi/abs/10.1002/sca.4950050301

  9. Torrance R, James D (2011) The state-of-the-art in semiconductor reverse engineering. In: 2011 48th ACM/EDAC/IEEE Design Automation Conference (DAC), pp 333–338

  10. Vashistha N, Lu H, Shi Q, Rahman MT, Shen H, Woodard DL, Asadizanjani N, Tehranipoor M (2018) Trojan scanner: Detecting hardware trojans with rapid sem imaging combined with image processing and machine learning. In: ISTFA 2018: Proceedings from the 44th International Symposium for Testing and Failure Analysis, ASM International, p 256

  11. Waite AR, Patel Y, Kelley JJ, Scholl JH, Baur J, Kimura A, Udelhoven ED, Via GD, Ott R, Brooks DL (2021) Preparation, imaging, and design extraction of the front-end-of-line and middle-of-line in a 14 nm node finfet device. In: 2021 IEEE Physical Assurance and Inspection of Electronics (PAINE), pp 1–6

  12. Wilson R, Lu H, Zhu M, Forte D, Woodard DL (2021) Refics: Assimilating data-driven paradigms into reverse engineering and hardware assurance on integrated circuits. IEEE Access 9:131955–131976

    Article  Google Scholar 

  13. Grochowski A, Bhattacharya D, Viswanathan T, Laker K (1997) Integrated circuit testing for quality assurance in manufacturing: history, current status, and future trends. IEEE Trans Circuits Syst II Analog Digit Signal Process 44(8):610–633

    Article  Google Scholar 

  14. Ahi K, Asadizanjani N, Shahbazmohamadi S, Tehranipoor M, Anwar M (2015) Terahertz characterization of electronic components and comparison of terahertz imaging with x-ray imaging techniques. In: Terahertz Physics, Devices, and Systems IX: Advanced Applications in Industry and Defense, International Society for Optics and Photonics, vol 9483, p 94830K

  15. Houdek C, Design C (2016) Inspection and testing methods for pcbs: An overview. Engineer/OwnerCaltronics Design & Assembly

  16. Mahmood K, Carmona PL, Shahbazmohamadi S, Pla F, Javidi B (2015) Real-time automated counterfeit integrated circuit detection using x-ray microscopy. Appl Opt 54(13):D25–D32. https://doi.org/10.1364/AO.54.000D25, http://opg.optica.org/ao/abstract.cfm?URI=ao-54-13-D25

  17. Bao C, Forte D, Srivastava A (2015) Temperature tracking: Toward robust run-time detection of hardware trojans. IEEE Trans Comput Aided Des Integr Circuits Syst 34(10):1577–1585. https://doi.org/10.1109/TCAD.2015.2424929

    Article  Google Scholar 

  18. Agrawal D, Baktir S, Karakoyunlu D, Rohatgi P, Sunar B (2007) Trojan detection using ic fingerprinting. In: 2007 IEEE Symposium on Security and Privacy (SP ’07), pp 296–310. https://doi.org/10.1109/SP.2007.36

  19. Banga M, Hsiao MS (2008) A region based approach for the identification of hardware trojans. 2008 IEEE International Workshop on Hardware-Oriented Security and Trust pp 40–47

  20. Hossain FS, Shintani M, Inoue M, Orailoglu A (2018) Variation-aware hardware trojan detection through power side-channel. In: 2018 IEEE International Test Conference (ITC), pp 1–10. https://doi.org/10.1109/TEST.2018.8624866

  21. Shende R, Ambawade DD (2016) A side channel based power analysis technique for hardware trojan detection using statistical learning approach. In: 2016 Thirteenth International Conference on Wireless and Optical Communications Networks (WOCN), pp 1–4. https://doi.org/10.1109/WOCN.2016.7759894

  22. Lyu Y, Mishra P (2020) Automated test generation for trojan detection using delay-based side channel analysis. In: 2020 Design, Automation Test in Europe Conference Exhibition (DATE), pp 1031–1036. https://doi.org/10.23919/DATE48585.2020.9116461

  23. He J, Zhao Y, Guo X, Jin Y (2017) Hardware trojan detection through chip-free electromagnetic side-channel statistical analysis. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 25(10):2939–2948. https://doi.org/10.1109/TVLSI.2017.2727985

  24. Ngo XT, Najm Z, Bhasin S, Guilley S, Danger JL (2016) Method taking into account process dispersion to detect hardware trojan horse by side-channel analysis. J Cryptogr Eng 6(3):239–247

    Article  Google Scholar 

  25. Balasch J, Gierlichs B, Verbauwhede I (2015) Electromagnetic circuit fingerprints for hardware trojan detection. In: 2015 IEEE International Symposium on Electromagnetic Compatibility (EMC), pp 246–251. https://doi.org/10.1109/ISEMC.2015.7256167

  26. Boyer C, Roy S (2014) invited paper - backscatter communication and rfid: Coding, energy, and mimo analysis. IEEE Trans Commun 62(3):770–785. https://doi.org/10.1109/TCOMM.2013.120713.130417

    Article  Google Scholar 

  27. Landt J (2005) The history of rfid. IEEE Potentials 24(4):8–11. https://doi.org/10.1109/MP.2005.1549751

    Article  Google Scholar 

  28. Adibelli S, Juyal P, Nguyen LN, Prvulovic M, Zajić A (2020) Near-field backscattering-based sensing for hardware trojan detection. IEEE Trans Antennas Propag 68(12):8082–8090. https://doi.org/10.1109/TAP.2020.3000562

    Article  Google Scholar 

  29. Nguyen LN, Cheng CL, Prvulovic M, Zajić A (2019) Creating a backscattering side channel to enable detection of dormant hardware trojans. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 27(7):1561–1574. https://doi.org/10.1109/TVLSI.2019.2906547

  30. Chakraborty RS, Narasimhan S, Bhunia S (2009) Hardware trojan: Threats and emerging solutions. In: 2009 IEEE International High Level Design Validation and Test Workshop, pp 166–171. https://doi.org/10.1109/HLDVT.2009.5340158

  31. Venugopalan V, Patterson CD (2018) Surveying the hardware trojan threat landscape for the internet-of-things. Journal of Hardware and Systems Security 2(2):131–141

    Article  Google Scholar 

  32. 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, pp 50–57. https://doi.org/10.1109/HST.2009.5224971

  33. Karri R, Rajendran J, Rosenfeld K, Tehranipoor M (2010) Trustworthy hardware: Identifying and classifying hardware trojans. Computer 43(10):39–46. https://doi.org/10.1109/MC.2010.299

    Article  Google Scholar 

  34. Shakya B, He T, Salmani H, Forte D, Bhunia S, Tehranipoor M (2017) Benchmarking of hardware trojans and maliciously affected circuits. Journal of Hardware and Systems Security 1(1):85–102. https://doi.org/10.1007/s41635-017-0001-6

    Article  Google Scholar 

  35. Zhang J, Yuan F, Xu Q (2014) Detrust: Defeating hardware trust verification with stealthy implicitly-triggered hardware trojans. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery, New York, NY, USA, CCS ’14, p 153–166. https://doi.org/10.1145/2660267.2660289

  36. Rajendran J, Sinanoglu O, Karri R (2014) Regaining trust in vlsi design: Design-for-trust techniques. Proc IEEE 102(8):1266–1282. https://doi.org/10.1109/JPROC.2014.2332154

    Article  Google Scholar 

  37. Mal-Sarkar S, Karam R, Narasimhan S, Ghosh A, Krishna A, Bhunia S (2016) Design and validation for fpga trust under hardware trojan attacks. IEEE Transactions on Multi-Scale Computing Systems 2(3):186–198. https://doi.org/10.1109/TMSCS.2016.2584052

    Article  Google Scholar 

  38. Tehranipoor M, Salmani H, Zhang X, Wang M, Karri R, Rajendran J, Rosenfeld K (2011) Trustworthy hardware: Trojan detection and design-for-trust challenges. Computer 44(7):66–74. https://doi.org/10.1109/MC.2010.369

    Article  Google Scholar 

  39. Agrawal D, Archambeult B (2002) The em side-channel(s). Proc Crypto HW and Emb Sys (CHES) pp 29–45

  40. Rohatgi P (2008) Electromagnetic attacks and countermeasures, Wiley, pp 407–430. https://doi.org/10.1007/978-0-387-71817-0_15

  41. Callan R, Zajić A, Prvulovic M (2013) A practical methodology for measure the side-channel signal available to the attacker for instruction level events. IEEE MICRO 14:1–12

    Article  Google Scholar 

  42. Nikitin P, Rao K (2006) Theory and measurement of backscattering from rfid tags. IEEE Antennas Propag Mag 48(6):212–218. https://doi.org/10.1109/MAP.2006.323323

    Article  Google Scholar 

  43. Rabaey JM, Chandrakasan AP, Nikolic B (2002) Digital integrated circuits, vol 2. Prentice hall Englewood Cliffs

  44. Chang CI (2003) Hyperspectral imaging: techniques for spectral detection and classification, vol 1. Springer Science & Business Media

  45. Grahn H, Geladi P (2007) Techniques and applications of hyperspectral image analysis. John Wiley & Sons

    Book  Google Scholar 

  46. Goetz AF (2009) Three decades of hyperspectral remote sensing of the earth: A personal view. Remote Sens Environ 113:S5–S16

    Article  Google Scholar 

  47. Carrasco O, Gomez RB, Chainani A, Roper WE (2003) Hyperspectral imaging applied to medical diagnoses and food safety. Geo-Spatial and Temporal Image and Data Exploitation III, International Society for Optics and Photonics 5097:215–221

    Google Scholar 

  48. Lu G, Fei B (2014) Medical hyperspectral imaging: a review. Journal of Biomedical Optics 19(1):1–24. https://doi.org/10.1117/1.JBO.19.1.010901, https://doi.org/10.1117/1.JBO.19.1.010901

  49. Carvalho RR, Coelho JA, Santos JM, Aquino FW, Carneiro RL, Pereira-Filho ER (2015) Laser-induced breakdown spectroscopy (LIBS) combined with hyperspectral imaging for the evaluation of printed circuit board composition. Talanta 134:278–283. https://doi.org/10.1016/j.talanta.2014.11.019, https://www.sciencedirect.com/science/article/pii/S0039914014009114

  50. Palmieri R, Bonifazi G, Serranti S (2014) Recycling-oriented characterization of plastic frames and printed circuit boards from mobile phones by electronic and chemical imaging. Waste Management 34(11), 2120–2130. https://doi.org/10.1016/j.wasman.2014.06.003, https://www.sciencedirect.com/science/article/pii/S0956053X1400258X

  51. Cerna M, Harvey AF (2000) The fundamentals of fft-based signal analysis and measurement. Tech. rep., Application Note 041, National Instruments

  52. Intel: Glossary. Available at https://www.intel.com/content/www/us/en/programmable/quartushelp/17.0/reference/glossary/glosslist.htm#. Accessed 28 Apr 2022

  53. terasIC: De0-cv board. Available at https://www.terasic.com.tw/cgi-bin/page/archive.pl?Language=English&CategoryNo=163&No=921. Accessed 16 Feb 2022

  54. Intel: Cyclone v device overview. Available at https://www.intel.com/content/dam/www/programmable/us/en/pdfs/literature/hb/cyclone-v/cv_51001.pdf. Accessed 27 May 2021

  55. 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), pp 471–474. https://doi.org/10.1109/ICCD.2013.6657085

  56. Zaber: X-lsq150b specifications. Available at https://www.zaber.com/products/linear-stages/X-LSQ/specs?part=X-LSQ150B. Accessed 02 May 2022

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Funding

This work has been supported, in part, by Office of Naval Research grant N00014-19-1-2287. The views and finding in this paper are those of the authors and do not reflect the views of ONR.

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Correspondence to Erik J. Jorgensen.

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Jorgensen, E.J., Kacmarcik, A., Prvulovic, M. et al. Novel Feature Selection for Non-destructive Detection of Hardware Trojans Using Hyperspectral Scanning. J Hardw Syst Secur 6, 32–46 (2022). https://doi.org/10.1007/s41635-022-00127-7

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  • DOI: https://doi.org/10.1007/s41635-022-00127-7

Keywords

  • Hardware Trojan
  • Hardware security
  • Backscattering EM side-channel
  • Active learning
  • Hyperspectral imaging