研究業績

人工知能ロフタル学会誌Vol.100 1, 2, 3-10 (2020).
人工知能と社会

著者

mem

キーワード

人工知能,確率推論

カテゴリ

国内学会

Abstract

A stream cipher is an important class of encryption algorithms. Its safety depends on the structure of the pseudorandom number generator used. There are various types of pseudo-random number generators in existence, and attack algorithms used on them have been studied individually. In this paper, we express the problem of attacks on a general stream cipher as a probabilistic inference problem, and formulate the optimal key estimation. We also propose a unified framework of attack algorithms that can be applied to a wide variety of stream ciphers. The optimal key estimation, however, has computational complexity. To reduce the complexity, an approximation algorithm based on a probabilistic inference is proposed. We also describe some attack algorithms used on practical pseudorandom number generators. Finally, the proposed algorithm is evaluated by through a computer simulation.
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