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Differential fault location identification by machine learning

CAAI Transactions on Intelligence Technology 2021年第1期6卷 17-24页

作者： Anubhab Baksi Santanu Sarkar akhilesh siddhanti Ravi Anand Anupam Chattopadhyay Nanyang Technological University Singapore Indian Institute of Technology ChennaiIndia Georgia Institute of Technology AtlantaUSA Indian Institute of Technology KharagpurIndia

As the fault‐based attacks are becoming a more pertinent threat in today's era of edge computing/internet‐of‐things,there is a need to streamline the existing tools for better accuracy and ease of use,so that we can gauge the attacker's power and a proper countermeasure can be devised in the long *** this regard,we propose a machine learning(ML)assisted tool that can be used in the context of a differential fault *** particular,finding the exact fault location by analysing the output difference(typically the XOR of the nonfaulty and the faulty ciphertexts)is somewhat *** the literature survey,we notice that the Pearson's correlation coefficient dominantly is used for this purpose,and has almost become the defacto *** this method can yield good accuracy for certain cases,we argue that an MLbased method is more powerful in all the situations we experiment *** sub-stantiate our claim by showing the relative performances(we choose the commonly used multilayer perceptron as our ML tool)with two variants of Grain‐128a(a stream cipher,and a stream cipher with authentication),the lightweight stream cipher LIZARD and the lightweight block cipher SIMON‐32(where the faults are injected at the fifth last rounds).Our results demonstrate that a common ML tool can outperform the correlation with the same training/testing *** believe that our work extends the state‐of‐the‐art by showing how traditional cryptographic methods can be replaced by a more powerful ML tool.

关键词： fault typically gauge

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