In one dimension, there is essentially just one binomially   distributed statistic, bias or correlation, for testing correctness   of a key bit in Matsui's Algorithm 1. In multiple dimensions,   different statistical approaches for finding the correct key   candidate are available. The purpose of this work is to investigate   the efficiency of such test in theory and practice, and propose a   new key class ranking statistic using distributions based on   multidimensional linear approximation and generalisation of the   ranking statistic presented by Selc cuk.
['Miia Hermelin', 'Joo Yeon Cho', 'Kaisa Nyberg']
Statistical Tests for Key Recovery Using Multidimensional Extension of Matsui's Algorithm 1.