Abstract
Side channel attacks are recognized as one of the most powerful attacks due to their ability to extract secret key information by analyzing the unintended leakage generated during operation. This makes them highly attractive for attackers. The current countermeasures focus on either randomizing the leakage by obfuscating the power consumption of all operations or blinding the leakage by maintaining a similar power consumption for all operations. Although these techniques help hiding the power-leakage correlation, they do not remove the correlation completely. This paper proposes a new countermeasure type, referred to as confusion, that aims to break the linear correlation between the leakage model and the power consumption and hence confuses attackers. It realizes this by replacing the traditional SBOX implementation with a neural network referred to as S-NET. As a case study, the security of Advanced Encryption Standard (AES) software implementations with both conventional SBOX and S-NET are evaluated. Based on our experimental results, S-NET leaks no information and is resilient against popular attacks such as differential and correlation power analysis.
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References
NIST: Announcing the advanced encryption standard (AES). Fed. Inf. Process. Stan. Publ. 197 3 (2001)
Leech, D.P., et al.: The economic impacts of the advanced encryption standard, 1996–2017. NIST (2018)
IBM: 2019 Cost of a Data Breach Report: IBM Security (2019).https://databreachcalculator.mybluemix.net/. Accessed 23 Sept 2019
Ors, S.B., et al.: Power analysis attack on an ASIC AES implementation. In: ITCC (2004)
Chari, S., et al.: Towards sound approaches to counteract power-analysis attacks. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 398–412. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48405-1_26
Kocher, P., et al.: Differential power analysis. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 388–397. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48405-1_25
Messerges, T.S.: Securing the AES finalists against power analysis attacks. In: Goos, G., Hartmanis, J., van Leeuwen, J., Schneier, B. (eds.) FSE 2000. LNCS, vol. 1978, pp. 150–164. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44706-7_11
Coron, J.S., et al.: Higher-order side channel security and mask refreshing. In: Moriai, S. (ed.) FSE 2013. LNCS, vol. 8424, pp. 410–424. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43933-3_21
Durvaux, F., et al.: Efficient removal of random delays from embedded software implementations using hidden Markov models. In: Mangard, S. (ed.) CARDIS 2012. LNCS, vol. 7771, pp. 123–140. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37288-9_9
Luo, P., et al.: Towards secure cryptographic software implementation against side-channel power analysis attacks. In: ASAP (2015)
Durvaux, F., et al.: Cryptanalysis of the CHES 2009/2010 random delay countermeasure. IACR Cryptol. ePrint Arch. 2012, 38 (2012)
Veyrat-Charvillon, N., et al.: Shuffling against Side-channel attacks: a comprehensive study with cautionary note. In: Wang, X., Sako, K. (eds.) ASIACRYPT 2012. LNCS, vol. 7658, pp. 740–757. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34961-4_44
Tiri, K., et al.: A dynamic and differential CMOS logic with signal independent power consumption to withstand differential power analysis on smart cards. In: 28th European Solid-State Circuits Conference (2002)
Ambrose, J.A., et al.: MUTE-AES: a multiprocessor architecture to prevent power analysis based side channel attack of the AES algorithm. In: ICCD (2008)
Fang, X., et al.: Leakage evaluation on power balance countermeasure against side-channel attack on FPGAs. In: IEEE HPEC (2015)
Shannon, C.E.: Communication theory of secrecy systems. The Bell Syst. Tech. J. 25(4), 656–715 (1949)
Zhou, Y., Feng, D.: Side-channel attacks: ten years after its publication and the impacts on cryptographic module security testing. http://eprint.iacr.org/2005/388. Accessed 23 Sept 2019
Brier, E., et al.: Correlation power analysis with a leakage model. In: Joye, M., Quisquater, J.-J. (eds.) CHES 2004. LNCS, vol. 3156, pp. 16–29. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28632-5_2
Chari, S., et al.: Template attacks. In: Kaliski, B.S., Koç, K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 13–28. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36400-5_3
Maghrebi, H., et al.: Breaking cryptographic implementations using deep learning techniques. In: IACR Cryptology ePrint Archive (2016)
Hassoun, M.H.: Fundamentals of Artificial Neural Networks. MIT Press, Cambridge (1995)
Csáji, B.C.: Approximation with artificial neural networks. Master’s thesis, Eötvös Loránd University, Hungary (2001)
Standaert, F.-X.: Introduction to Side-Channel Attacks. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-71829-3_2
N. T. Inc: Chipwhisperer-Lite two part board. http://store.newae.com/chipwhisperer-lite-cw1173-two-part-version/. Accessed 31 Jan 2020
Becker, G., et al.: Test vector leakage assessment ( TVLA ) methodology in practice (2011). https://pdfs.semanticscholar.org/. Accessed 23 Sept 2019
Mangard, S.: Hardware countermeasures against DPA – a statistical analysis of their effectiveness. In: Okamoto, T. (ed.) CT-RSA 2004. LNCS, vol. 2964, pp. 222–235. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24660-2_18
N. technology inc: Measuring SNR of Target. https://chipwhisperer.readthedocs.io/en/latest/tutorials/pa_intro_3-openadc-cwlitearm.html/. Accessed 13 May 2020
Rivain, M., Prouff, E.: Provably secure higher-order masking of AES. In: Mangard, S., Standaert, F.-X. (eds.) CHES 2010. LNCS, vol. 6225, pp. 413–427. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15031-9_28
Sun, J.: CMOS and memristor technologies for neuromorphic computing applications. Technical report, University of California at Berkeley (2015)
Acknowledgments
This work was labelled by the EUREKA cluster PENTA and funded by Dutch authorities under grant agreement PENTA-2018e-17004-SunRISE.
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Aljuffri, A., Venkatachalam, P., Reinbrecht, C., Hamdioui, S., Taouil, M. (2020). S-NET: A Confusion Based Countermeasure Against Power Attacks for SBOX. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2020. Lecture Notes in Computer Science(), vol 12471. Springer, Cham. https://doi.org/10.1007/978-3-030-60939-9_20
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