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A Robust Machine Learning Approach to Microprocessor Instructions Identification

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Book cover Advances in Soft Computing (MICAI 2016)

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Abstract

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 robust 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 good accuracy. In addition, we provide a comparison between several performance and recognition enhancement tools.

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References

  1. 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). doi:10.1007/978-3-540-45203-4_24

    Chapter  Google Scholar 

  2. Clavier, C.: Side channel analysis for reverse engineering (SCARE) -an improved attack against a secret A3/A8 GSM algorithm. In: 2004 IACR Cryptology ePrint Archive, vol. 49 (2004)

    Google Scholar 

  3. Réal, D., Dubois, V., Guilloux, A.-M., Valette, F., Drissi, M.: SCARE of an unknown hardware feistel implementation. In: Grimaud, G., Standaert, F.-X. (eds.) CARDIS 2008. LNCS, vol. 5189, pp. 218–227. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85893-5_16

    Chapter  Google Scholar 

  4. Rivain, M., Roche, T.: SCARE of secret ciphers with SPN structures. In: Sako, K., Sarkar, P. (eds.) ASIACRYPT 2013. LNCS, vol. 8269, pp. 526–544. Springer, Heidelberg (2013). doi:10.1007/978-3-642-42033-7_27

    Chapter  Google Scholar 

  5. Clavier, C., Isorez, Q., Wurcker, A.: Complete SCARE of AES-like block ciphers by chosen plaintext collision power analysis. In: Paul, G., Vaudenay, S. (eds.) INDOCRYPT 2013. LNCS, vol. 8250, pp. 116–135. Springer, Cham (2013). doi:10.1007/978-3-319-03515-4_8

    Chapter  Google Scholar 

  6. Quisquater, J.-J., Samyde, D.: Electromagnetic analysis (EMA): measures and counter-measures for smart cards. In: Attali, I., Jensen, T. (eds.) E-smart 2001. LNCS, vol. 2140, pp. 200–210. Springer, Heidelberg (2001). doi:10.1007/3-540-45418-7_17

    Chapter  Google Scholar 

  7. Goldack, M.: Side-channel based reverse engineering for microcontrollers. Diploma Thesis, Ruhr-University Bochum (2008). https://www.emsec.rub.de/media/attachments/files/2012/10/da_goldack.pdf

  8. Eisenbarth, T., Kasper, T., Moradi, A., Paar, C., Salmasizadeh, M., Shalmani, M.T.M.: on the power of power analysis in the real world: a complete break of the KeeLoq code hopping scheme. In: Wagner, D. (ed.) CRYPTO 2008. LNCS, vol. 5157, pp. 203–220. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85174-5_12

    Chapter  Google Scholar 

  9. Msgna, M., Markantonakis, K., Mayes, K.: Precise instruction-level side channel profiling of embedded processors. In: Huang, X., Zhou, J. (eds.) ISPEC 2014. LNCS, vol. 8434, pp. 129–143. Springer, Cham (2014). doi:10.1007/978-3-319-06320-1_11

    Chapter  Google Scholar 

  10. 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)

    Google Scholar 

  11. Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electron. Imaging 16(4), 33–46 (2007)

    Google Scholar 

  12. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)

    MATH  Google Scholar 

  13. 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). doi:10.1007/978-3-540-31815-6_35

    Chapter  Google Scholar 

  14. Mousa, A., Hamad, A.: Evaluation of the RC4 algorithm for data encryption. IJCSA 3(2), 44–56 (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Smart Token Research Group, Modelling and Digital Science (MDS), Council for Scientific and Industrial Research (CSIR), Pretoria, South Africa

    Hippolyte Djonon Tsague

  2. Department of Electrical and Electronic Engineering Science, Faculty of Engineering, Institute for Intelligent Systems, University of Johannesburg (UJ), Johannesburg, South Africa

    Bheki Twala

Authors
  1. Hippolyte Djonon Tsague
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  2. Bheki Twala
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Corresponding author

Correspondence to Hippolyte Djonon Tsague .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, México DF, Mexico

    Obdulia Pichardo-Lagunas

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

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Tsague, H.D., Twala, B. (2017). A Robust Machine Learning Approach to Microprocessor Instructions Identification. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-62428-0_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62427-3

  • Online ISBN: 978-3-319-62428-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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