• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.109-116
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• 2007

In this paper, we analyse the security of the variant of Self-Shrinking generator proposed by Chang et al. against a guess-and-determine attack. This variant, which we call SSG-XOR is claimed to have better cryptographic properties than the Self-Shrinking generator in a practical setting. But we show that SSG-XOR is weaker than the Self-Shrinking generator from the viewpoint of guess-and-determine attack.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.2
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• pp.25-32
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• 2006

In this paper, we propose a fast correlation attack on the shrinking and self-shrinking generator. This attack is an improved algorithm or the fast correlation attack by Zhang et al. at CT-RSA 2005. For the shrinking generator, we recover the initial state of generating LFSR whose length is 61 with $2^{15.43}$ keystream bits, the computational complexity of $2^{56.3314}$ and success probability 99.9%. We also recover the initial state of generating LFSR whose length is $2^{40}$ of the self-shrinking generator with $2^{45.89}$ keystream bits, the computational complexity of $2^{112.424}$ and success probability 99.9%.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.88-91
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• 2002

자기 수축 발생기(self-shrinking generator)는 Meier와 Staffelbach에 의해 제안되었으며[4], 구조가 간단하고 키수열을 생성하는 속도가 빠르기 때문에 스트림 암호시스템으로 각광받고 있다 [5]. 본 논문에서는 자기 수축 발생기의 새로운 구성방법을 제안한다. 제안된 자기 수축 발생기는 하나의 선형귀환회로와 주어진 짝수 m에 의하여 정의되며 일반적으로 선형귀환회로의 귀환다항식으로 원시다항식을 사용한다. 이 경우 키수열은 균형성을 만족하며, 선형귀환회로의 귀환다항식의 차수를 $d_{Y}$ 라고 하면 주기는 $d_{Y-2}$ 이다. m을 $2^{η}$ζ로 표현하면 선형복잡도 Lz는 $d_{Y}$ ＋η-3/$\leq$ $L_{Z}$ $\leq$m/2($d_{Y}$ -1 - ($d_{Y}$ -2))이다. 따라서 제안된 자기 수축 발생기는 기존의 자기 수축 발생기에 비하여 암호학적으로 우수한 성질을 갖는다.다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.322-327
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• 2003

The water level of a steam generator of pressurized light water nuclear Power generator is known as a subject whose control is difficult because of a shrinking and swelling effect that is been mutually contradictory in a variation of feed water. In this paper, a neural network model selects first coordinative controller by a inappropriate gain of two PI controllers and the selected controller＇s gain is tuned by a fuzzy self-tuner. Model inputs consist of the water level, the feed water, and the stream flow. One controller of both coupling controllers whose gain is handled firstly is decided based upon above data. The proposed method can analyze patterns of signals using the characteristic of neural networks and select one controller that needs to be tuned through the observed result in this paper. If one controller between both the water level controller and the feed water controller is selected by the neural network model then a gain of the PI controller is suitably tuned by the fuzzy self-tuner. Rules of the fuzzy self-tuner drew from the pattern of input and output data. In the summary, the goal of this Paper is to select the suitable controller and tune the control gain of the selected controller suitably through such two processes.