Optimal Key Ranking Procedures in a Statistical Cryptanalysis

Hypothesis tests have been used in the past as a tool in a cryptanalytic context. In this paper, we propose to use this paradigm and define a precise and sound statistical framework in order to optimally mix information on independent attacked subkey bits obtained from any kind of statistical cryptanalysis. In the context of linear cryptanalysis, we prove that the best mixing paradigm consists of sorting key candidates by decreasing weighted Euclidean norm of the bias vector.