Paper 2023/1109

An End-to-end Plaintext-based Side-channel Collision Attack without Trace Segmentation

Lichao Wu, Delft University of Technology
Sébastien Tiran, Independent Researcher
Guilherme Perin, Leiden University
Stjepan Picek, Radboud University Nijmegen

Side-channel Collision Attacks (SCCA) constitute a subset of non-profiling attacks that exploit information dependency leaked during cryptographic operations. Unlike traditional collision attacks, which seek instances where two different inputs to a cryptographic algorithm yield identical outputs, SCCAs specifically target the internal state, where identical outputs are more likely. In CHES 2023, Staib et al. presented a Deep Learning-based SCCA (DL-SCCA), which enhanced the attack performance while decreasing the required effort for leakage preprocessing. Nevertheless, this method inherits the conventional SCCA's limitations, as it operates on trace segments reflecting the target operation explicitly, leading to issues such as portability and low tolerance to errors. This paper introduces an end-to-end plaintext-based SCCA to address these challenges. We leverage the bijective relationship between plaintext and secret data to label the leakage measurement with known information, then learn plaintext-based profiling models to depict leakages from varying operations. By comparing the leakage representations produced by the profiling model, an adversary can reveal the key difference. As an end-to-end approach, we propose an error correction scheme to rectify false predictions. Experimental results indicate our approach significantly surpasses DL-SCCA in terms of attack performance (e.g., success rate increased from 53\% to 100\%) and computational complexity (training time reduced from approximately 2 hours to 10 minutes). These findings underscore our method's effectiveness and practicality in real-world attack scenarios.

Available format(s)
Attacks and cryptanalysis
Publication info
Side-channel AnalysisSide-channel Collision AttackDeep Learning
Contact author(s)
lichao wu9 @ gmail com
sebastien tiran @ gmail com
guilhermeperin7 @ gmail com
picek stjepan @ gmail com
2023-07-17: approved
2023-07-16: received
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Creative Commons Attribution


      author = {Lichao Wu and Sébastien Tiran and Guilherme Perin and Stjepan Picek},
      title = {An End-to-end Plaintext-based Side-channel Collision Attack without Trace Segmentation},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1109},
      year = {2023},
      note = {\url{}},
      url = {}
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