Abstract
Electronic devices that populate the Internet of Things play increasingly important roles in our everyday lives. When these devices process, store, or communicate personal or company-critical data, digital security becomes a necessity. However, mechanisms to secure electronic systems have a significant influence on the cost of the system and come with an overhead in energy consumption, computational delay, and (silicon) chip area. Therefore, developing secure electronic systems is a balancing act between minimizing the overhead and maximizing the security. Moreover, in rapidly evolving markets, there is another parameter that can have a negative influence on the security strength of electronic devices, namely the time to market: it takes longer to bring a secure product to the market than to develop a product with no or little security measures in place.
In the PROACT project, we tackle the challenge of maximizing the security strength while minimizing the overhead w.r.t. energy consumption, computational delay, and hardware resources, as well as reducing the time to market of digital electronic systems. We specifically focus on the fast development of efficient cryptographic hardware with protection against physical attacks, i.e., attacks that exploit the physical implementation of cryptographic algorithms. Physical attacks are categorized into (1) side-channel analysis attacks that target the extraction of secret information by monitoring side-channels like the power consumption, the electromagnetic emanation or the timing of the device, and (2) fault analysis attacks that aim at introducing computational errors that lead to the leakage of secret information. Physical security is of vital importance when potential attackers can easily get in the vicinity of an electronic system. This is the case in, e.g., medical sensor devices, wearables and implants, which are typically constrained in energy budget, cost and form factor, and are therefore the perfect use case for the results of PROACT.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
FIPS 197: Advanced Encryption Standard (AES) (2021). https://doi.org/10.6028/NIST.FIPS.197-upd1
Aagaard, M.D., Zidaric, N.: ASIC benchmarking of round 2 candidates in the NIST lightweight cryptography standardization process: (preliminary results). IACR Cryptol. ePrint Arch., p. 49 (2021). https://eprint.iacr.org/2021/049
Adhikary, A., Buhan, I.: SoK: assisted fault simulation - existing challenges and opportunities offered by AI. In: Zhou, J., et al. (eds.) ACNS 2023. LNCS, vol. 13907, pp. 178–195. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-41181-6_10
Azouaoui, M., et al.: A systematic appraisal of side channel evaluation strategies. In: van der Merwe, T., Mitchell, C., Mehrnezhad, M. (eds.) SSR 2020. LNCS, vol. 12529, pp. 46–66. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64357-7_3
Bazangani, O., Iooss, A., Buhan, I., Batina, L.: ABBY: automating leakage modeling for side-channels analysis. In: Proceedings of the 2024 ACM Asia Conference on Computer and Communications Security (2024, to appear)
Buhan, I., Batina, L., Yarom, Y., Schaumont, P.: SoK: design tools for side-channel-aware implementations. In: Suga, Y., Sakurai, K., Ding, X., Sako, K. (eds.) ASIA CCS 2022: ACM Asia Conference on Computer and Communications Security, Nagasaki, Japan, 30 May 2022–3 June 2022, pp. 756–770. ACM (2022). https://doi.org/10.1145/3488932.3517415
Christoph Dobraunig, I.: Ascon-a submission to CAESAR. In: 15th Central European Conference on Cryptology, p. 23 (2015)
Daemen, J., Dobraunig, C., Eichlseder, M., Groß, H., Mendel, F., Primas, R.: Protecting against statistical ineffective fault attacks. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2020(3), 508–543 (2020). https://doi.org/10.13154/TCHES.V2020.I3.508-543
Daemen, J., Hoffert, S., Peeters, M., Assche, G.V., Keer, R.V.: Xoodyak, a lightweight cryptographic scheme (2020)
Daemen, J., Massolino, P.M.C., Mehrdad, A., Rotella, Y.: The subterranean 2.0 cipher suite. IACR Trans. Symmetric Cryptol. 2020(S1), 262–294 (2020). https://doi.org/10.13154/TOSC.V2020.IS1.262-294
Daemen, J., Rijmen, V.: The Design of Rijndael - The Advanced Encryption Standard (AES). Information Security and Cryptography, 2nd edn. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-662-60769-5
Davide Schiavone, P., et al.: Slow and steady wins the race? A comparison of ultra-low-power RISC-V cores for internet-of-things applications. In: 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS), pp. 1–8 (2017). https://doi.org/10.1109/PATMOS.2017.8106976
Dobraunig, C., Eichlseder, M., Korak, T., Mangard, S., Mendel, F., Primas, R.: SIFA: exploiting ineffective fault inductions on symmetric cryptography. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2018(3), 547–572 (2018)
Karayalcin, S., Krcek, M., Wu, L., Picek, S., Perin, G.: It’s a kind of magic: a novel conditional GAN framework for efficient profiling side-channel analysis. Cryptology ePrint Archive, Paper 2023/1108 (2023). https://eprint.iacr.org/2023/1108
Kulkarni, P., Verneuil, V., Picek, S., Batina, L.: Order vs. chaos: a language model approach for side-channel attacks. Cryptology ePrint Archive, Paper 2023/1615 (2023). https://eprint.iacr.org/2023/1615
LeMay, M., et al.: Cryptographic capability computing. In: MICRO 2021, pp. 253–267. ACM (2021)
Miteloudi, K., Batina, L., Daemen, J., Mentens, N.: ROCKY: rotation countermeasure for the protection of keys and other sensitive data. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds.) SAMOS 2021. LNCS, vol. 13227, pp. 288–299. Springer, Cham (2021). https://doi.org/10.1007/978-3-031-04580-6_19
Perin, G., Wu, L., Picek, S.: Exploring feature selection scenarios for deep learning-based side-channel analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2022(4), 828–861 (2022). https://doi.org/10.46586/tches.v2022.i4.828-861. https://tches.iacr.org/index.php/TCHES/article/view/9842
Perin, G., Wu, L., Picek, S.: I know what your layers did: layer-wise explainability of deep learning side-channel analysis. Cryptology ePrint Archive, Paper 2022/1087 (2022). https://eprint.iacr.org/2022/1087
Picek, S., Perin, G., Mariot, L., Wu, L., Batina, L.: SoK: deep learning-based physical side-channel analysis. ACM Comput. Surv. 55(11), 1–35 (2023). https://doi.org/10.1145/3569577
Rijsdijk, J., Wu, L., Perin, G., Picek, S.: Reinforcement learning for hyperparameter tuning in deep learning-based side-channel analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2021(3), 677–707 (2021). https://doi.org/10.46586/tches.v2021.i3.677-707. https://tches.iacr.org/index.php/TCHES/article/view/8989
Savu, I., Krček, M., Perin, G., Wu, L., Picek, S.: The need for more: unsupervised side-channel analysis with single network training and multi-output regression. Cryptology ePrint Archive, Paper 2023/1681 (2023). https://eprint.iacr.org/2023/1681
Shelton, M.A., Samwel, N., Batina, L., Regazzoni, F., Wagner, M., Yarom, Y.: Rosita: towards automatic elimination of power-analysis leakage in ciphers. In: Proceedings 2021 Network and Distributed System Security Symposium. Internet Society, Virtual (2021). https://doi.org/10.14722/ndss.2021.23137. https://www.ndss-symposium.org/wp-content/uploads/ndss2021_4B-3_23137_paper.pdf
Wu, L., Ali-pour, A., Rezaeezade, A., Perin, G., Picek, S.: Breaking free: leakage model-free deep learning-based side-channel analysis. Cryptology ePrint Archive, Paper 2023/1110 (2023). https://eprint.iacr.org/2023/1110
Wu, L., Perin, G., Picek, S.: The best of two worlds: deep learning-assisted template attack. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2022(3), 413–437 (2022). https://doi.org/10.46586/tches.v2022.i3.413-437. https://tches.iacr.org/index.php/TCHES/article/view/9707
Wu, L., Perin, G., Picek, S.: I choose you: automated hyperparameter tuning for deep learning-based side-channel analysis. IEEE Trans. Emerg. Top. Comput., 1–12 (2022). https://doi.org/10.1109/TETC.2022.3218372
Wu, L., Perin, G., Picek, S.: Hiding in plain sight: non-profiling deep learning-based side-channel analysis with plaintext/ciphertext. Cryptology ePrint Archive, Paper 2023/209 (2023). https://eprint.iacr.org/2023/209
Wu, L., Tiran, S., Perin, G., Picek, S.: An end-to-end plaintext-based side-channel collision attack without trace segmentation. Cryptology ePrint Archive, Paper 2023/1109 (2023). https://eprint.iacr.org/2023/1109
Wu, L., et al.: Label correlation in deep learning-based side-channel analysis. IEEE Trans. Inf. Forensics Secur. 18, 3849–3861 (2023). https://doi.org/10.1109/TIFS.2023.3287728
Yap, T., Picek, S., Bhasin, S.: Beyond the last layer: deep feature loss functions in side-channel analysis. In: Proceedings of the 2023 Workshop on Attacks and Solutions in Hardware Security, ASHES 2023, pp. 73–82. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3605769.3623996
Acknowledgements
This work was funded by the Dutch Research Council (NWO) through the PROACT project (NWA.1215.18.014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Adhikary, A. et al. (2024). PROACT - Physical Attack Resistance of Cryptographic Algorithms and Circuits with Reduced Time to Market. In: Skliarova, I., Brox Jiménez, P., Véstias, M., Diniz, P.C. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2024. Lecture Notes in Computer Science, vol 14553. Springer, Cham. https://doi.org/10.1007/978-3-031-55673-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-55673-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-55672-2
Online ISBN: 978-3-031-55673-9
eBook Packages: Computer ScienceComputer Science (R0)