Statistical dependencies in the Self-Shrinking Generator

Boztaş, Serdar; Alamer, Ahmed

doi:10.1109/iwsda.2015.7458410

Cited by 5 publications

(8 citation statements)

References 12 publications

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“…For the algorithm for this test and other monomial tests, see [14], in which these tests to IV-based ciphers using IV were applied. In our case, we used the initial LFSRs' loading, while authors in [15] performed this test on the SSG, which is a variant of the SG. However, the test had to be adapted because there are two LFSRs in the SG to generate the keystream but only one in the SSG.…”

Section: A Our Approach To the D-monomial Testmentioning

confidence: 99%

Design and Implementation of a Statistical Testing Framework for a Lightweight Stream Cipher

Alamer

Soh

2020

Eng. Technol. Appl. Sci. Res.

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The Shrinking Generator (SG) is a popular synchronous, lightweight stream cipher that uses minimal computing power. However, its strengths and weaknesses have not been studied in detail. This paper proposes a statistical testing framework to assess attacks on the SG. The framework consists of a d-monomial test that is adapted to SG by applying the algebraic normal form (ANF) representation of Boolean functions, a test that uses the maximal degree monomial test to determine whether the ANF follows the proper mixing of bit values, and a proposed unique window size (UWS) scheme to test the randomness properties of the keystream. The proposed framework shows significant weaknesses in the SG output in terms of dependence between the controlling linear-feedback shift register (LFSR) and non-linearity of the resulting keystream. The maximal degree monomial test provides a better understanding of the optimal points of SG, demonstrating when it is at its best and worst according to the first couple of results. This paper uses UWS to illustrate the effect of the LFSR choice on possibly distinguishing attacks on the SG. The results confirm that the proposed UWS scheme is a viable measure of the cryptographic strength of a stream cipher. Due to the importance of predictability and effective tools, we used neural network models to simulate the input data for the pseudo-random binary sequences. Through the calculation of UWS, we obtained solid results for the predictions.

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Section: A Our Approach To the D-monomial Testmentioning

confidence: 99%

Design and Implementation of a Statistical Testing Framework for a Lightweight Stream Cipher

Alamer

Soh

2020

Eng. Technol. Appl. Sci. Res.

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“…The results have shown that both generators have good statistical properties. In Reference [21] the authors have studied the randomness of the Self-Shrinking generator by means of the d-Monomial test. They have found that there exist some statistical dependencies on certain randomness properties of the generalized SSG and polynomial used in its design.…”

Section: Related Workmentioning

confidence: 99%

“…It is worth mentioning that the design of reliable and secure pseudorandom number generators is an open problem and an intensive field of research in cryptography nowadays [23][24][25][26][27][28]. The family of shrinking generators is one of the most analyzed PRNG in the literature due to its performance and security when it is well designed [4,21,22,[29][30][31].…”

Section: Preliminariesmentioning

confidence: 99%

Randomness Analysis for the Generalized Self-Shrinking Sequences

et al. 2019

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In cryptography, the property of randomness in pseudo-random generators is very important to avoid any pattern in output sequences, to provide security against attacks, privacy and anonymity. In this article, the randomness of the family of sequences obtained from the generalized self-shrinking generator is analyzed. Moreover, the characteristics, generalities and relationship between the t-modified self-shrinking generator and the generalized self-shrinking generator are presented. We find that the t-modified self-shrunken sequences can be generated from a generalized self-shrinking generator. Then, an in-depth analysis of randomness focused on the generalized sequences by means of complete and powerful batteries of statistical tests and graphical tools is done, providing a useful vision of the behaviour of these sequences and proving that they are suitable to be used in cryptography.

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“…On the other hand, if is is not the initial state used in the generation of the intercepted bits, then by [16,Theorem 1] the consistency probability of the system will be very small when the intercepted segment is long enough. In order to make the number of false consistency alarms as small as possible, the number of equations in (2) should exceed L + L signi cantly, see [16] and [17]. The attack is divided into two phases.…”

Section: Cryptanalytic Attackmentioning

confidence: 99%

Linearity in decimation-based generators: an improved cryptanalysis on the shrinking generator

2018

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Decimation-based sequence generators are a class of non-linear cryptographic generators designed to be used in hardware implementations. An inherent characteristic of such generators is that their output sequences are interleaved sequences. This profitable characteristic can be used in the cryptanalysis of those generators. In this work, emphasis is on the most representative decimation-based generator, the shrinking generator, which has been cryptanalyzed just by solving linear equation systems. Compared with previous cryptanalysis, computational complexity and intercepted sequence requirements are dramatically reduced. Although irregularly decimated generators have been conceived and designed as non-linear sequence generators, in practice they can be easily analyzed in terms of simple linear structures.

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Statistical dependencies in the Self-Shrinking Generator

Cited by 5 publications

References 12 publications

Design and Implementation of a Statistical Testing Framework for a Lightweight Stream Cipher

Design and Implementation of a Statistical Testing Framework for a Lightweight Stream Cipher

Randomness Analysis for the Generalized Self-Shrinking Sequences

Linearity in decimation-based generators: an improved cryptanalysis on the shrinking generator

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