nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

A Fast Characterization Method for Semi-invasive Fault Injection Attacks

verfasst von : Lichao Wu, Gerard Ribera, Noemie Beringuier-Boher, Stjepan Picek

Erschienen in: Topics in Cryptology – CT-RSA 2020

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Semi-invasive fault injection attacks are powerful techniques well-known by attackers and secure embedded system designers. When performing such attacks, the selection of the fault injection parameters is of utmost importance and usually based on the experience of the attacker. Surprisingly, there exists no formal and general approach to characterize the target behavior under attack. In this work, we present a novel methodology to perform a fast characterization of the fault injection impact on a target, depending on the possible attack parameters. We experimentally show our methodology to be a successful one when targeting different algorithms such as DES and AES encryption and then extend to the full characterization with the help of deep learning. Finally, we show how the characterization results are transferable between different targets.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Extending NIST’s CAVP Testing of Cryptographic Hash Function Implementations

Nächstes Kapitel Tightly Secure Two-Pass Authenticated Key Exchange Protocol in the CK Model

Skorobogatov, S.P., Anderson, R.J.: Optical fault induction attacks. In: Kaliski, B.S., Koç, K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 2–12. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36400-5_2CrossRef

Carpi, R.B., Picek, S., Batina, L., Menarini, F., Jakobovic, D., Golub, M.: Glitch it if you can: parameter search strategies for successful fault injection. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 236–252. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08302-5_16CrossRef

Picek, S., Batina, L., Jakobović, D., Carpi, R.B.: Evolving genetic algorithms for fault injection attacks. In: 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1106–1111. IEEE (2014)

Picek, S., Batina, L., Buzing, P., Jakobovic, D.: Fault injection with a new flavor: memetic algorithms make a difference. In: Mangard, S., Poschmann, A.Y. (eds.) COSADE 2014. LNCS, vol. 9064, pp. 159–173. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21476-4_11CrossRef

Boneh, D., DeMillo, R.A., Lipton, R.J.: On the importance of checking cryptographic protocols for faults. In: Fumy, W. (ed.) EUROCRYPT 1997. LNCS, vol. 1233, pp. 37–51. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-69053-0_4CrossRef

Kömmerling, O., Kuhn, M.G.: Design principles for tamper-resistant smartcard processors. In: Proceedings of the USENIX Workshop on Smartcard Technology on USENIX Workshop on Smartcard Technology, p. 2. Berkeley, CA, USA, USENIX Association (1999)

Skorobogatov, S.: Optical fault masking attacks. In: 2010 Workshop on Fault Diagnosis and Tolerance in Cryptography, pp. 23–29. August 2010

van Woudenberg, J.G.J., Witteman, M.F., Menarini, F.: Practical optical fault injection on secure microcontrollers. In: 2011 Workshop on Fault Diagnosis and Tolerance in Cryptography, pp. 91–99. September 2011

Leveugle, R., et al.: Laser-induced fault effects in security-dedicated circuits. In: 2014 22nd International Conference on Very Large Scale Integration (VLSI-SoC), pp. 1–6. IEEE (2014)

10.

Guillen, O.M., Gruber, M., De Santis, F.: Low-cost setup for localized semi-invasive optical fault injection attacks. In: Guilley, S. (ed.) COSADE 2017. LNCS, vol. 10348, pp. 207–222. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64647-3_13CrossRef

11.

Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures - profiling attacks without pre-processing. In: Proceedings of International Conference on Cryptographic Hardware and Embedded Systems - CHES 2017–19th, Taipei, Taiwan, 25–28 September 2017, pp. 45–68 (2017)

12.

Picek, S., Heuser, A., Jovic, A., Bhasin, S., Regazzoni, F.: The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(1), 209–237 (2019)

13.

Kim, J., Picek, S., Heuser, A., Bhasin, S., Hanjalic, A.: Make some noise. unleashing the power of convolutional neural networks for profiled side-channel analysis. IACR Trans. Cryptographic Hardware Embed. Syst. 2019(3), 148–179 (2019)

14.

Maldini, A., Samwel, N., Picek, S., Batina, L.: Genetic algorithm-based electromagnetic fault injection. In: 2018 Workshop on Fault Diagnosis and Tolerance in Cryptography (FDTC), pp. 35–42. September 2018

15.

Zhou, Y.B., Feng, D.G.: Side-channel attacks: ten years after its publication and the impacts on cryptographic module security testing. IACR Cryptol. ePrint Archive 2005, 388 (2005)

16.

Tria, A., Choukri, H.: Invasive attacks. In: van Tilborg, H.C.A., Jajodia, S. (eds.) Encyclopedia of Cryptography and Security, pp. 623–629. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-5906-5CrossRef

17.

Kumar, R., Jovanovic, P., Polian, I.: Precise fault-injections using voltage and temperature manipulation for differential cryptanalysis. In: 2014 IEEE 20th International On-Line Testing Symposium (IOLTS), pp. 43–48. IEEE (2014)

18.

Picek, S., et al.: Side-channel analysis and machine learning: a practical perspective. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 4095–4102. IEEE (2017)

19.

Skorobogatov, S.P.: Semi-invasive attacks: a new approach to hardware security analysis (2005)

20.

Johnston, A.H.: Charge generation and collection in PN junctions excited with pulsed infrared lasers. IEEE Trans. Nuclear Sci. 40(6), 1694–1702 (1993)CrossRef

21.

Merli, D., Schuster, D., Stumpf, F., Sigl, G.: Semi-invasive EM attack on FGPA RO PUFs and countermeasures. In: Proceedings of the Workshop on Embedded Systems Security, WESS 2011, pp. 2:1–2:9, New York, NY, USA, ACM (2011)

22.

Beringuier-Boher, N., Lacruche, M., El-Baze, D., Dutertre, J.-M., Rigaud, J.-B., Maurine, P.: Body biasing injection attacks in practice. In: Proceedings of the Third Workshop on Cryptography and Security in Computing Systems, pp. 49–54. ACM (2016)

23.

Gurney, K.: An Introduction to Neural Networks. CRC Press, Boca Raton (2014)CrossRef

24.

Collobert, R., Bengio, S.: Links between perceptrons, MLPs and SVMs. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, p. 23. New York, NY, USA, ACM (2004)

25.

LeNail, A.: NN-SVG: publication-ready neural network architecture schematics. J. Open Source Softw. 4(33), 747 (2019)CrossRef

26.

Biham, E., Shamir, A.: Differential fault analysis of secret key cryptosystems. In: Kaliski, B.S. (ed.) CRYPTO 1997. LNCS, vol. 1294, pp. 513–525. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0052259CrossRef

27.

Giraud, C.: DFA on AES. In: Dobbertin, H., Rijmen, V., Sowa, A. (eds.) AES 2004. LNCS, vol. 3373, pp. 27–41. Springer, Heidelberg (2005). https://doi.org/10.1007/11506447_4CrossRef

Titel: A Fast Characterization Method for Semi-invasive Fault Injection Attacks
verfasst von: Lichao Wu
Gerard Ribera
Noemie Beringuier-Boher
Stjepan Picek
Verlag: Springer International Publishing
Buch: Topics in Cryptology – CT-RSA 2020
Print ISBN: 978-3-030-40185-6

Electronic ISBN: 978-3-030-40186-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-40186-3_8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"