Precise Instruction-Level Side Channel Profiling of Embedded Processors

  • Mehari Msgna
  • Konstantinos Markantonakis
  • Keith Mayes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8434)


Since the first publication, side channel leakage has been widely used for the purposes of extracting secret information, such as cryptographic keys, from embedded devices. However, in a few instances it has been utilised for extracting other information about the internal state of a computing device. In this paper, we show how to create a precise instruction-level side channel leakage profile of an embedded processor. Using the profile we show how to extract executed instructions from the device’s leakage with high accuracy. In addition, we provide a comparison between several performance and recognition enhancement tools. Further, we also provide details of our lab setup and noise minimisation techniques, and suggest possible applications.


Side Channel Leakage Templates Principal Components Analysis Linear Discriminant Analysis Multivariate Gaussian Distribution k-Nearest Neighbors Algorithm Reverse Engineering 


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  1. 1.
    Kocher, P., Jaffe, J., Jun, B.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Oswald, D., Paar, C.: Breaking mifare DESFire MF3ICD40: Power analysis and templates in the real world. In: Preneel, B., Takagi, T. (eds.) CHES 2011. LNCS, vol. 6917, pp. 207–222. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Kocher, P.C.: Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In: Koblitz, N. (ed.) CRYPTO 1996. LNCS, vol. 1109, pp. 104–113. Springer, Heidelberg (1996)Google Scholar
  4. 4.
    Dhem, J.-F., Koeune, F., Leroux, P.-A., Mestré, P., Quisquater, J.-J., Willems, J.-L.: A practical implementation of the timing attack. In: Quisquater, J.-J., Schneier, B. (eds.) CARDIS 1998. LNCS, vol. 1820, pp. 167–182. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Heyszl, J., Mangard, S., Heinz, B., Stumpf, F., Sigl, G.: Localized electromagnetic analysis of cryptographic implementations. In: Dunkelman, O. (ed.) CT-RSA 2012. LNCS, vol. 7178, pp. 231–244. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Van Eck, W., Laborato, N.: Electromagnetic radiation from video display units: An eavesdropping risk? Computers & Security 4, 269–286 (1985)CrossRefGoogle Scholar
  7. 7.
    Novak, R.: Side-channel attack on substitution blocks. In: Zhou, J., Yung, M., Han, Y. (eds.) ACNS 2003. LNCS, vol. 2846, pp. 307–318. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Clavier, C.: Side channel analysis for reverse engineering (SCARE) - An improved attack against a secret A3/A8 GSM algorithm. IACR Cryptology ePrint Archive 2004, 49 (2004)Google Scholar
  9. 9.
    Vermoen, D., Witteman, M., Gaydadjiev, G.N.: Reverse engineering Java Card applets using power analysis. In: Sauveron, D., Markantonakis, K., Bilas, A., Quisquater, J.-J. (eds.) WISTP 2007. LNCS, vol. 4462, pp. 138–149. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Quisquater, J.-J., Samyde, D.: Automatic code recognition for smartcards using a kohonen neural network. In: Proceedings of the Fifth Smart Card Research and Advanced Application Conference, CARDIS 2002, November 21-22. USENIX (2002)Google Scholar
  11. 11.
    Eisenbarth, T., Paar, C., Weghenkel, B.: Building a side channel based disassembler. In: Gavrilova, M.L., Tan, C.J.K., Moreno, E.D. (eds.) Transactions on Computational Science X. LNCS, vol. 6340, pp. 78–99. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Standaert, F.-X., Archambeau, C.: Using subspace-based template attacks to compare and combine power and electromagnetic information leakages. In: Oswald, E., Rohatgi, P. (eds.) CHES 2008. LNCS, vol. 5154, pp. 411–425. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electronic Imaging 16(4) (2007)Google Scholar
  14. 14.
    Rechberger, C., Oswald, E.: Practical template attacks. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 440–456. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Mousa, A., Hamad, A.: Evaluation of the RC4 algorithm for data encryption. IJCSA 3(2), 44–56 (2006)Google Scholar
  16. 16.
    Berrendero, J.R., Justel, A., Svarc, M.: Principal components for multivariate functional data. Computational Statistics & Data Analysis 55(9), 2619–2634 (2011)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Strang, G.: Introduction to Linear Algebra, 3rd edn. Wellesley-Cambridge Press, MA (2003)Google Scholar
  18. 18.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
  19. 19.
    Fukumi, M., Mitsukura, Y.: Feature generation by simple-FLDA for pattern recognition. In: CIMCA/IAWTIC, November 28-30, pp. 730–734. IEEE Computer Society (2005)Google Scholar
  20. 20.
    Zhang, L., Wang, D., Gao, S.: Application of improved Fisher Linear Discriminant Analysis approaches. In: International Conference on Management Science and Industrial Engineering (MSIE), pp. 1311–1314 (2011)Google Scholar
  21. 21.
    Gut, A.: An Intermediate Course In Probability, 2nd edn. Springer, Department of Mathematics, Uppsala University, Sweden (2009)Google Scholar
  22. 22.
    Wang, L., Zhang, Y., Feng, J.: On the Euclidean distance of images. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1334–1339 (2005)CrossRefGoogle Scholar
  23. 23.
    Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer (2009)Google Scholar
  24. 24.
    Web site. Tutorial for learning assembly language for the AVR-Single-Chip-Processors, (visited October 2013)
  25. 25.
    Web site. AVR freaks, (visited October 2013)
  26. 26.
    LeCroy, T.: Teledyne LeCroy website, (visited February 2013)
  27. 27.
    Pomona Electronics. 6069A scope probe, website, (visited October 2012)
  28. 28.
    Kohenen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)CrossRefGoogle Scholar
  29. 29.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  30. 30.
    Kohenen, T.: Learning Vector Quantization. Springer (2001)Google Scholar
  31. 31.
    Rish, I.: An empirical study of the naive bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3(22), pp. 41–46 (August 2001)Google Scholar
  32. 32.
    National Institute of Standards and Technology. Data encryption standard (DES), publication 46-3. Technical report, Department of Commerce (Reaffirmed October 1999),
  33. 33.
    National Institute of Standards and Technology. Advanced encryption standard (AES), publication 197. Technical report, Department of Commerce (November 2001),
  34. 34.
    Coron, J.-S., Goubin, L.: On boolean and arithmetic masking against differential power analysis. In: Koç, Ç.K., Paar, C. (eds.) CHES 2000. LNCS, vol. 1965, pp. 231–237. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  35. 35.
    Yu, B., Li, X., Chen, C., Sun, Y., Wu, L., Zhang, X.: An AES chip with DPA resistance using hardware-based random order execution. Journal of Semiconductors 33(6) (2012)Google Scholar
  36. 36.
    Clavier, C., Coron, J.-S., Dabbous, N.: Differential power analysis in the presence of hardware countermeasures. In: Koç, Ç.K., Paar, C. (eds.) CHES 2000. LNCS, vol. 1965, pp. 252–263. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mehari Msgna
    • 1
  • Konstantinos Markantonakis
    • 1
  • Keith Mayes
    • 1
  1. 1.Smart Card Centre, Information Security Group, Royal HollowayUniversity of LondonSurreyUK

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