Security Evaluation of Cryptographic Modules against Profiling Attacks

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Recently, profiling attacks have been attracting a great deal of attention because of their increasing efficiency. Further investigations are required to determine the potential threats of the profiling attacks. This paper focuses on these attacks. Using hardware and software implementations, we provide a security evaluation of three different types of profiling attacks: template attack, stochastic model attack, and multivariate regression attack. Our experimental results show that multivariate regression attack outperforms other attacks in terms of profiling efficiency and key extraction rates.

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