Welcome to the resource topic for 2025/440
Title:
AI for Code-based Cryptography
Authors: Mohamed Malhou, Ludovic Perret, Kristin Lauter
Abstract:We introduce the use of machine learning in the cryptanalysis of code-based cryptography. Our focus is on distinguishing problems related to the security of NIST round-4 McEliece-like cryptosystems, particularly for Goppa codes used in ClassicMcEliece and Quasi-Cyclic Moderate Density Parity-Check (QC-MDPC) codes used in BIKE. We present DeepDistinguisher, a new algorithm for distinguishing structured codes from random linear codes that uses a transformer. The results show that the new distinguisher achieves a high level of accuracy in distinguishing Goppa codes, suggesting that their structure may be more recognizable by AI models. Our approach outperforms traditional attacks in distinguishing Goppa codes in certain settings and does generalize to larger code lengths without further training using a puncturing technique. We also present the first distinguishing results dedicated to MDPC and QC-MDPC codes.
ePrint: https://eprint.iacr.org/2025/440
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