Linear cryptanalysis method for DES cipher
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A New Version of the Stream Cipher SNOW
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CRYPTO '02 Proceedings of the 22nd Annual International Cryptology Conference on Advances in Cryptology
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ASIACRYPT'05 Proceedings of the 11th international conference on Theory and Application of Cryptology and Information Security
Resistance of SNOW 2.0 against algebraic attacks
CT-RSA'05 Proceedings of the 2005 international conference on Topics in Cryptology
Algebraic Description and Simultaneous Linear Approximations of Addition in Snow 2.0.
ICICS '08 Proceedings of the 10th International Conference on Information and Communications Security
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Improved linear cryptanalysis of SOSEMANUK
ICISC'09 Proceedings of the 12th international conference on Information security and cryptology
Multiset collision attacks on reduced-round SNOW 3G and SNOW 3G⊕
ACNS'10 Proceedings of the 8th international conference on Applied cryptography and network security
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ISC'10 Proceedings of the 13th international conference on Information security
Linear approximations of addition modulo 2n-1
FSE'11 Proceedings of the 18th international conference on Fast software encryption
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Multidimensional linear distinguishing attacks and Boolean functions
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Synthetic linear analysis with applications to CubeHash and Rabbit
Cryptography and Communications
Determining quality of s-boxes using pseudo random sequences generated from stream ciphers
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FPS'12 Proceedings of the 5th international conference on Foundations and Practice of Security
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In this paper we present new and more accurate estimates of the biases of the linear approximation of the FSM of the stream cipher SNOW 2.0. Based on improved bias estimates we also find a new linear distinguisher with bias 2−−86.9 that is significantly stronger than the previously found ones by Watanabe et al. (2003) and makes it possible to distinguish the output keystream of SNOW 2.0 of length 2174 words from a truly random sequence with workload 2174. This attack is also stronger than the recent distinguishing attack by Maximov and Johansson (2005). We also investigate the diffusion properties of the MixColumn transformation used in the FSM of SNOW 2.0 and present some evidence why much more efficient distinguishers may not exist.