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- Amparo Fúster-Sabater, Maria Eugenia Pazo-Robles, Pino Caballero-Gil
- Neural Networks
- 2010

In this work, it is shown that the output sequence of a well-known cryptographic generator, the so-called self-shrinking generator, can be obtained from a simple linear model based on cellular automata. In fact, such a cellular model is a linear version of a nonlinear keystream generator currently used in stream ciphers. The linearization procedure is… (More)

- Amparo Fúster-Sabater, Pino Caballero-Gil, Maria Eugenia Pazo-Robles
- EUROCAST
- 2007

- Pino Caballero-Gil, Amparo Fúster-Sabater, Maria Eugenia Pazo-Robles
- Journal of Research and Practice in Information…
- 2008

This work shows that the cryptanalysis of the shrinking generator requires fewer intercepted bits than that indicated by the linear complexity. Indeed, whereas the linear complexity of shrunken sequences is between A·2 (S−2) and A·2 (S−1) , we claim that the initial states of both component registers are easily computed with less than A · S shrunken bits.… (More)

- Amparo Fúster-Sabater, Pino Caballero-Gil, Maria Eugenia Pazo-Robles
- International Conference on Computational Science
- 2006

A new class of linear sequence generators based on cellular automata is here introduced in order to model several nonlinear keystream generators with practical applications in symmetric cryptography. The output sequences are written as solutions of linear difference equations, and three basic properties (period, linear complexity and number of different… (More)

- Maria Eugenia Pazo-Robles, Amparo Fúster-Sabater
- ICANNGA
- 2011

- Maria Eugenia Pazo-Robles, Amparo Fúster-Sabater
- 2011 IEEE EUROCON - International Conference on…
- 2011

In this paper, a cryptanalytic attack on a class of cryptographic sequence generators (the class of Self-Shrinking Generators) is presented. In fact, the guess and determine cryptanalytic technique has been used as well as numerical results that improve other cryptanalysis developed on such a generator are shown. In particular, the complexities here… (More)

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