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Multiple-chi-square tests and their application on distinguishing attacks

机译:多卡方检验及其在区分攻击中的应用

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Chi-square tests are vastly used for distinguishing random distributions, but extra care must be taken when using them on several independent variables. We noticed, the chisquare statistics, in some previous works, was computed half of its real value. Thus, to avoid possible future confusions, we formulize multiple-chi-square tests. To show the application of multiple-chi-square tests, we introduce two new tests and apply them to Trivium as a special case. These tests are modifications of ANF monomial test and, when applied to Trivium with the same number of rounds, the data complexity of them is roughly 24 times smaller than that of previous ANF monomial test.
机译:卡方检验广泛用于区分随机分布,但是在几个独立变量上使用卡方检验时必须格外小心。我们注意到,在以前的一些工作中,卡方统计量是其实际价值的一半。因此,为了避免将来可能出现的混乱,我们制定了多重卡方检验。为了展示多重卡方检验的应用,我们引入了两个新的检验,并将它们作为特例应用于Trivium。这些测试是对ANF单项测试的修改,当以相同的回合数应用于Trivium时,它们的数据复杂度比以前的ANF单项测试小大约2 4 倍。

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