Skip to main content
SpringerLink
Log in

Machine Learning for Hardware Security: Opportunities and Risks

  • Published:
Journal of Electronic Testing Aims and scope Submit manuscript

Abstract

Recently, machine learning algorithms have been utilized by system defenders and attackers to secure and attack hardware, respectively. In this work, we investigate the impact of machine learning on hardware security. We explore the defense and attack mechanisms for hardware that are based on machine learning. Moreover, we identify suitable machine learning algorithms for each category of hardware security problems. Finally, we highlight some important aspects related to the application of machine learning to hardware security problems and show how the practice of applying machine learning to hardware security problems has changed over the past decade.

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

Access this article

Log in via an institution

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
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Alam MM, Tehranipoor M, Forte D (2016) Recycled FPGA detection using exhaustive LUT path delay characterization. In: Proceedings of ITC, pp 1–10

  2. Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: ACM Sigmod record, vol 28, no. 2, pp 49–60

  3. Asadizanjani N, Tehranipoor M, Forte D (2017) Counterfeit electronics detection using image processing and machine learning. In: Journal of physics: conference series, vol 787, no. 1, p. 012023

  4. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, pp 37–49

  5. Bao C, Forte D, Srivastava A (2014) On application of one-class svm to reverse engineering-based hardware trojan detection. In: Proceedings of fifteenth international symposium on quality electronic design, pp 47–54

  6. Bao C, Forte D, Srivastava A (2016) On reverse engineering-based hardware trojan detection. IEEE Trans Comput Aided Des Integr Circuits Syst 35(1):49–57

    Article  Google Scholar 

  7. Barker WC, Barker E et al. (2012) Recommendation for the triple data encryption algorithm (tdea) block cipher: Nist special publication 800-67, revision 2

  8. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Information Processing-Letters and Reviews 11(10):203–224

    Google Scholar 

  9. Çakir B, Malik S (2015) Hardware Trojan detection for gate-level ICs using signal correlation based clustering. In: Proceedings of DATE, pp 471–476

  10. Carvalho VR, Cohen WW (2006) Single-pass online learning: performance, voting schemes and online feature selection. In: Proceedings of ACM SIGKDD. ACM, pp 548–553

  11. Chari S, Rao JR, Rohatgi P (2002) Template attacks. In: CHES. Springer, pp 13–28

  12. Chen Q, Csaba G, Lugli P, Schlichtmann U, Rührmair U (2011) The bistable ring puf: a new architecture for strong physical unclonable functions. In: Proceedings of HOST. IEEE, pp 134– 141

  13. 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 Trans Comput Aided Des Integr Circuits Syst 36(10):1633–1646

    Article  Google Scholar 

  14. Choudary O, Kuhn MG (2013) Efficient template attacks. In: CARDIS. Springer, pp 253–270

  15. Cox DR (1958) The regression analysis of binary sequences. J R Stat Soc Ser B Methodol 20(2):215–242

    MathSciNet  MATH  Google Scholar 

  16. Crammer K, Singer Y (2001) On the algorithmic implementation of multiclass kernel-based vector machines. J Mach Learn Res 2(Dec):265–292

    MATH  Google Scholar 

  17. Daemen J, Rijmen V (2001) Specification for the advanced encryption standard (aes). Federal Information Processing Standards Publication, vol 197

  18. Dogan H, Forte D, Tehranipoor MM (2014) Aging analysis for recycled FPGA detection. In: Proceedings of DFT, pp 171–176

  19. Eisenbarth T, Paar C, Weghenkel B (2010) Building a side channel based disassembler. In: Transactions on computational science x. Springer, pp 78–99

  20. Elnaggar R, Chakrabarty K, Tahoori MB (2017) Run-time hardware trojan detection using performance counters. In: Proceedings of ITC, pp 1–10

  21. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  MATH  Google Scholar 

  22. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67

    Article  MathSciNet  MATH  Google Scholar 

  23. Ganji F, Tajik S, Seifert J-P (2015) Let me prove it to you: Ro pufs are provably learnable. In: ICISC. Springer, pp 345–358

  24. Ganji F, Tajik S, Seifert J-P (2015) Why attackers win: on the learnability of xor arbiter pufs. In: Trust. Springer, pp 22–39

  25. Ganji F, Tajik S, Fäßler F, Seifert J-P (2016) Strong machine learning attack against pufs with no mathematical model. In: Proceedings of CHES. Springer, pp 391–411

  26. Ganji F, Tajik S, Seifert J-P (2016) Pac learning of arbiter pufs. J Cryptogr Eng 6(3):249–258

    Article  Google Scholar 

  27. Gers FA, Schraudolph NN, Schmidhuber J (2002) Learning precise timing with lstm recurrent networks. J Mach Learn Res 3(Aug):115–143

    MathSciNet  MATH  Google Scholar 

  28. Guin U, DiMase D, Tehranipoor M (2014) Counterfeit integrated circuits: detection, avoidance, and the challenges ahead. J Electron Test 30(1):9–23

    Article  Google Scholar 

  29. Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc: Ser C: Appl Stat 28(1):100– 108

    MATH  Google Scholar 

  30. Hasegawa K, Oya M, Yanagisawa M, Togawa N (2016) Hardware Trojans classification for gate-level netlists based on machine learning. In: Proceedings of IOLTS, pp 203–206

  31. Hasegawa K, Yanagisawa M, Togawa N (2017) Trojan-feature extraction at gate-level netlists and its application to hardware-trojan detection using random forest classifier. In: Proceedings of ISCAS. IEEE, pp 1–4

  32. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, Cambridge

    MATH  Google Scholar 

  33. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intelligent Systems and Their Applications 13(4):18–28

    Article  Google Scholar 

  34. Heuser A, Zohner M (2012) Intelligent machine homicide. In: COSADE. Springer, pp 249–264

  35. Heyszl J, Ibing A, Mangard S, De Santis F, Sigl G (2013) Clustering algorithms for non-profiled single-execution attacks on exponentiations. In: CARDIS. Springer, pp 79–93

  36. Ho TK (1995) Random decision forests. In: Proceedings of the third international conference on document analysis and recognition, vol 1. IEEE, pp 278–282

  37. Hospodar G, Gierlichs B, De Mulder E, Verbauwhede I, Vandewalle J (2011) Machine learning in side-channel analysis: a first study. J Cryptogr Eng 1(4):293–302

    Article  Google Scholar 

  38. Huang K, Carulli JM, Makris Y (2012) Parametric counterfeit IC detection via support vector machines. In: Proceedings of DFT, pp 7–12

  39. Huang L, Joseph AD, Nelson B, Rubinstein BI, Tygar J (2011) Adversarial machine learning. In: Proceedings of the 4th ACM workshop on security and artificial intelligence. ACM, pp 43–58

  40. Iwase T, Nozaki Y, Yoshikawa M, Kumaki T (2015) Detection technique for hardware Trojans using machine learning in frequency domain. In: Proceedings GCCE. IEEE, pp 185–186

  41. Jap D, Stöttinger M, Bhasin S (2015) Support vector regression: exploiting machine learning techniques for leakage modeling. In: Proceedings of HASP. ACM, p 2

  42. Jin Y, Maliuk D, Makris Y (2012) Post-deployment trust evaluation in wireless cryptographic ICs. In: Proceedings of DATE, pp 965–970

  43. Kaufman L, Rousseeuw PJ (1990) Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis, pp 68–125

  44. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI. Morgan Kaufmann Publishers Inc., pp 1137–1143

  45. Kohonen T (1998) The self-organizing map. Neurocomputing 21(1–3):1–6

    Article  MATH  Google Scholar 

  46. Kulkarni A, Pino Y, French M, Mohsenin T (2016) Real-time anomaly detection framework for many-core router through machine-learning techniques. ACM Journal on Emerging Technologies in Computing Systems (JETC) 13(1):10

    Google Scholar 

  47. Kulkarni A, Pino Y, Mohsenin T (2016) SVM-based real-time hardware trojan detection for many-core platform. In: Proceedings of ISQED, pp 362–367

  48. Kulkarni A, Pino Y, Mohsenin T (2016) Adaptive real-time Trojan detection framework through machine learning. In: Proceedings of HOST. IEEE, pp 120–123

  49. Lamichhane K, Moreno C, Fischmeister S (2018) Non-intrusive program tracing of non-preemptive multitasking systems using power consumption. In: Proceedings of DATE. Dresden, Germany

  50. LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time-series. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press

  51. Lerman L, Bontempi G, Markowitch O (2011) Side channel attack: an approach based on machine learning. Center for Advanced Security Research Darmstadt. 29–41

  52. Lerman L, Bontempi G, Taieb SB, Markowitch O (2013) A time series approach for profiling attack. In: SPACE. Springer, pp 75–94

  53. Lerman L, Medeiros SF, Veshchikov N, Meuter C, Bontempi G, Markowitch O (2013) Semi-supervised template attack. In: COSAD. Springer, pp 184–199

  54. Lerman L, Poussier R, Bontempi G, Markowitch O, Standaert F-X (2015) Template attacks vs. machine learning revisited (and the curse of dimensionality in side-channel analysis). In: COSADE. Springer, pp 20–33

  55. Li J, Cheng J-H, Shi J-Y, Huang F (2012) Brief introduction of back propagation (bp) neural network algorithm and its improvement. In: Advances in computer science and information engineering. Springer, pp 553–558

  56. Li J, Ni L, Chen J, Zhou E (2016) A novel hardware trojan detection based on bp neural network. In: 2016 2nd IEEE international conference on proc. computer and communications (ICCC). IEEE, pp 2790–2794

  57. Liu Y, Huang K, Makris Y (2014) Hardware trojan detection through golden chip-free statistical side-channel fingerprinting. In: Proceedings of DAC. ACM, pp 1–6

  58. Liu Y, Jin Y, Nosratinia A, Makris Y (2017) Silicon demonstration of hardware trojan design and detection in wireless cryptographic ICs. VLSI 25(4):1506–1519

    Article  Google Scholar 

  59. Loh W-Y (2011) Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(1):14–23

    Google Scholar 

  60. Maghrebi H, Portigliatti T, Prouff E (2016) Breaking cryptographic implementations using deep learning techniques. In: SPACE. Springer, pp 3–26

  61. Mandic DP, Chambers JA et al. (2001) Recurrent neural networks for prediction: learning algorithms, architectures and stability. Wiley Online Library

  62. Martinasek Z, Zeman V (2013) Innovative method of the power analysis. Radioengineering 22(2):586–594

    Google Scholar 

  63. Martinasek Z, Hajny J, Malina L (2013) Optimization of power analysis using neural network. In: CARDIS. Springer, pp 94–107

  64. Menezes AJ (2012) Elliptic curve public key cryptosystems, vol 234. Springer Science & Business Media, Berlin

    Google Scholar 

  65. Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, vol 821. Wiley, New York

    MATH  Google Scholar 

  66. O’Donnell R (2014) Analysis of boolean functions. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  67. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. TPAMI 27(8):1226–1238

    Article  Google Scholar 

  68. Quadir SE, Chen J, Forte D, Asadizanjani N, Shahbazmohamadi S, Wang L, Chandy J, Tehranipoor M (2016) A survey on chip to system reverse engineering. JETC 13(1):6

    Article  Google Scholar 

  69. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  70. Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier, Amsterdam

    Google Scholar 

  71. Rivest RL (1987) Learning decision lists. Mach Learn 2(3):229–246

    Google Scholar 

  72. Rivest RL (1991) Cryptography and machine learning. In: ASIACRYPT. Springer, pp 427–439

  73. Rostami M, Koushanfar F, Karri R (2014) A primer on hardware security: models, methods, and metrics. Proc IEEE 102(8):1283–1295

    Article  Google Scholar 

  74. Rührmair U, Sehnke F, Sölter J, Dror G, Devadas S, Schmidhuber J (2010) Modeling attacks on physical unclonable functions. In: Proceedings of CCS. ACM, pp 237–249

  75. Rührmair U, Sölter J, Sehnke F, Xu X, Mahmoud A, Stoyanova V, Dror G, Schmidhuber J, Burleson W, Devadas S (2013) Puf modeling attacks on simulated and silicon data. IEEE Trans Inf Forensics Secur 8(11):1876–1891

    Article  Google Scholar 

  76. Salmani H (2017) COTD: reference-free hardware trojan detection and recovery based on controllability and observability in gate-level netlist. IEEE Trans Inf Forensics Secur 12(2):338–350

    Article  Google Scholar 

  77. Schölkopf B, Burges CJ, Smola AJ (1999) Advances in kernel methods: support vector learning. MIT Press, Cambridge

    MATH  Google Scholar 

  78. Schölkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207–1245

    Article  Google Scholar 

  79. Scholkopf B, Sung K-K, Burges CJ, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45 (11):2758–2765

    Article  Google Scholar 

  80. Schuster D, Hesselbarth R (2014) Evaluation of bistable ring pufs using single layer neural networks. In: Proceedings of the international conference on trust and trustworthy computing. Springer, pp 101–109

  81. Shourong H, Yujie Z, Hongming L, Nianhao Z (2017) Wavelet support vector machine algorithm in power analysis attacks. Radioengineering 26(3):890–902

    Article  Google Scholar 

  82. Skabar A (2003) Single-class classifier learning using neural networks: an application to the prediction of mineral deposits. In: ICMLC, vol 4. IEEE, pp 2127–2132

  83. Spoild: side-channel power-based instruction-level disassembler. http://www.hostsymposium.org/host2017/hwdemo/HOST_2017_hwdemo_4.pdf. Accessed 15 Apr 2018

  84. Standaert F-X (2010) Introduction to side-channel attacks. In: Secure integrated circuits and systems. Springer, pp 27–42

  85. Standaert F-X, Koeune F, Schindler W (2009) How to compare profiled side-channel attacks?. In: ACNS. Springer, pp 485–498

  86. Standaert F-X, Malkin TG, Yung M (2009) A unified framework for the analysis of side-channel key recovery attacks. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 443–461

  87. Tan SC, Ting KM, Liu TF (2011) Fast anomaly detection for streaming data. In: Proceedings of IJCAI, pp 1511–1516

  88. Tax DM, Duin RP (2004) Support vector data description. Mach Learn 54(1):45–66

    Article  MATH  Google Scholar 

  89. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2 (1–3):37–52

    Article  Google Scholar 

  90. Xiao K, Forte D, Jin Y, Karri R, Bhunia S, Tehranipoor M (2016) Hardware trojans: lessons learned after one decade of research. TODAES 22(1):6

    Article  Google Scholar 

  91. Xue M, Wang J, Hux A (2016) An enhanced classification-based golden chips-free hardware trojan detection technique. In: Proceedings of Asian HOST, pp 1–6

Download references

Author information

Authors and Affiliations

  1. Departement of Electrical and Computer Engineering, Duke University, Durham, NC, 27705, USA

    Rana Elnaggar & Krishnendu Chakrabarty

Authors
  1. Rana Elnaggar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krishnendu Chakrabarty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rana Elnaggar.

Additional information

Responsible Editor: M. Tehranipoor

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elnaggar, R., Chakrabarty, K. Machine Learning for Hardware Security: Opportunities and Risks. J Electron Test 34, 183–201 (2018). https://doi.org/10.1007/s10836-018-5726-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10836-018-5726-9

Keywords

Search

Navigation