Paper 2022/306

The More You Know: Improving Laser Fault Injection with Prior Knowledge

Marina Krček, Thomas Ordas, Daniele Fronte, and Stjepan Picek

Abstract

We consider finding as many faults as possible on the target device in the laser fault injection security evaluation. Since the search space is large, we require efficient search methods. Recently, an evolutionary approach using a memetic algorithm was proposed and shown to find more interesting parameter combinations than random search, which is commonly used. Unfortunately, once a variation on the bench or target is introduced, the process must be repeated to find suitable parameter combinations anew. To negate the effect of variation, we propose a novel method combining memetic algorithm with machine learning approach called a decision tree. Our approach improves the memetic algorithm by using prior knowledge of the target introduced in the initial phase of the memetic algorithm. In our experiments, the decision tree rules enhance the performance of the memetic algorithm by finding more interesting faults on different samples of the same target. Our approach shows more than two orders of magnitude better performance than random search and up to 60% better performance than previous state-of-the-art results with a memetic algorithm. Another advantage of our approach is human-readable rules, allowing the first insights into the explainability of target characterization for laser fault injection.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint. MINOR revision.
Keywords
Laser Fault InjectionDecision TreeTransferability
Contact author(s)
m krcek @ tudelft nl
stjepan picek @ ru nl
History
2022-03-07: received
Short URL
https://ia.cr/2022/306
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2022/306,
      author = {Marina Krček and Thomas Ordas and Daniele Fronte and Stjepan Picek},
      title = {The More You Know: Improving Laser Fault Injection with Prior Knowledge},
      howpublished = {Cryptology ePrint Archive, Paper 2022/306},
      year = {2022},
      note = {\url{https://eprint.iacr.org/2022/306}},
      url = {https://eprint.iacr.org/2022/306}
}
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