Cryptology ePrint Archive: Report 2022/306

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

Marina Krček and Thomas Ordas and 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.

Category / Keywords: implementation / Laser Fault Injection, Decision Tree, Transferability

Date: received 5 Mar 2022, last revised 5 Mar 2022

Contact author: m krcek at tudelft nl, stjepan picek at ru nl

Available format(s): PDF | BibTeX Citation

Version: 20220307:124902 (All versions of this report)

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