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Paper: SIPFA: Statistical Ineffective Persistent Faults Analysis on Feistel Ciphers

Authors:
Nasour Bagheri , CPS2 lab., Shahid Rajaee Teacher Training University, Tehran, Iran; School of Computer Science (SCS), Institute for Research in Fundamental Sciences (IPM),Tehran, Iran
Sadegh Sadeghi
Prasanna Ravi , Temasek Laboratories, NTU, Singapore
Shivam Bhasin , Temasek Laboratories, NTU, Singapore
Hadi Soleimany , Cyber Research Center, Shahid Beheshti University, Tehran, Iran
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DOI: 10.46586/tches.v2022.i3.367-390
URL: https://tches.iacr.org/index.php/TCHES/article/view/9705
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Abstract: Persistent Fault Analysis (PFA) is an innovative and powerful analysis technique in which fault persists throughout the execution. The prior prominent results on PFA were on SPN block ciphers, and the security of Feistel ciphers against this attack has received less attention. In this paper, we introduce a framework to utilize Statistical Ineffective Fault Analysis (SIFA) in the persistent fault setting by proposing Statistical Ineffective Persistent Faults Analysis (SIPFA) that can be efficiently applied to Feistel ciphers in a variety of scenarios. To demonstrate the effectiveness of our technique, we apply SIFPA on three widely used Feistel schemes, DES, 3DES, and Camellia. Our analysis reveals that the secret key of these block ciphers can be extracted with a complexity of at most 250 utilizing a single unknown fault. Furthermore, we demonstrate that the secret can be recovered in a fraction of a second by increasing the adversary’s control over the injected faults. To evaluate SIPFA in a variety of scenarios, we conducted both simulations and real experiments utilizing electromagnetic fault injection on DES and 3DES.
BibTeX
@article{tches-2022-32070,
  title={SIPFA: Statistical Ineffective Persistent Faults Analysis on Feistel Ciphers},
  journal={IACR Transactions on Cryptographic Hardware and Embedded Systems},
  publisher={Ruhr-Universität Bochum},
  volume={2022, Issue 3},
  pages={367-390},
  url={https://tches.iacr.org/index.php/TCHES/article/view/9705},
  doi={10.46586/tches.v2022.i3.367-390},
  author={Nasour Bagheri and Sadegh Sadeghi and Prasanna Ravi and Shivam Bhasin and Hadi Soleimany},
  year=2022
}