A Simple Computational Model for Acceptance/Rejection of Binary Sequence Generators

A simple binary model to compute the degree of balancedness in the output sequence of LFSR-combinational generators has been developed. The computational method is based exclusively on the handling of binary strings by means of logic operations. The proposed model can serve as a deterministic alternative to existing probabilistic methods for checking balancedness in binary sequence generators. The procedure here described can be devised as a first selective criterium for acceptance/rejection of this type of generators.Comment: 16 pages, 0 figure

Cryptographic properties of Boolean functions defining elementary cellular automata

In this work, the algebraic properties of the local transition functions of elementary cellular automata (ECA) were analysed. Specifically, a classification of such cellular automata was done according to their algebraic degree, the balancedness, the resiliency, nonlinearity, the propagation criterion and the existence of non-zero linear structures. It is shown that there is not any ECA satisfying all properties at the same time

JPEG2000 compatible neural network based cipher

In this paper, an efficient encryption technique is proposed, especially for JPEG2000 compatible images.The technique uses a multilayer neural network to generate a pseudo-random sequence for transforming wavelet subbands into cipher subbands.The neural network generator takes 64 bit key as a startup seed with additional 64 bit key for initial weights and biases.At each layer, output is calculated by several iterations to increase the complexity of the pseudorandom sequence generation.In order to examine effectiveness of this approach, various tests including correlation, histogram, key space etc. are conducted on test images, and the results demonstrate the robustness of the proposed approach

Perfectly Balanced Boolean Functions and Golić Conjecture

Golić conjecture states that the necessary condition for a function to be perfectly balanced for any choice of a tapping sequence is linearity of a function in the first or in the last essential variable. In the current paper we prove Golić conjecture

Algorithm 959: VBF: A Library of C plus plus Classes for Vector Boolean Functions in Cryptography

VBF is a collection of C++ classes designed for analyzing vector Boolean functions (functions that map a Boolean vector to another Boolean vector) from a cryptographic perspective. This implementation uses the NTL library from Victor Shoup, adding new modules that call NTL functions and complement the existing ones, making it better suited to cryptography. The class representing a vector Boolean function can be initialized by several alternative types of data structures such as Truth Table, Trace Representation, and Algebraic Normal Form (ANF), among others. The most relevant cryptographic criteria for both block and stream ciphers as well as for hash functions can be evaluated with VBF: it obtains the nonlinearity, linearity distance, algebraic degree, linear structures, and frequency distribution of the absolute values of the Walsh Spectrum or the Autocorrelation Spectrum, among others. In addition, operations such as equality testing, composition, inversion, sum, direct sum, bricklayering (parallel application of vector Boolean functions as employed in Rijndael cipher), and adding coordinate functions of two vector Boolean functions are presented. Finally, three real applications of the library are described: the first one analyzes the KASUMI block cipher, the second one analyzes the Mini-AES cipher, and the third one finds Boolean functions with very high nonlinearity, a key property for robustness against linear attacks

Artificial Intelligence for the design of symmetric cryptographic primitives

Algorithms and the Foundations of Software technolog

A classification of S-boxes generated by orthogonal cellular automata

Most of the approaches published in the literature to construct S-boxes via Cellular Automata (CA) work by either iterating a finite CA for several time steps, or by a one-shot application of the global rule. The main characteristic that brings together these works is that they employ a single CA rule to define the vectorial Boolean function of the S-box. In this work, we explore a different direction for the design of S-boxes that leverages on Orthogonal CA (OCA), i.e. pairs of CA rules giving rise to orthogonal Latin squares. The motivation stands on the facts that an OCA pair already defines a bijective transformation, and moreover the orthogonality property of the resulting Latin squares ensures a minimum amount of diffusion. We exhaustively enumerate all S-boxes generated by OCA pairs of diameter 4≤d≤6, and measure their nonlinearity. Interestingly, we observe that for d=4 and d=5 all S-boxes are linear, despite the underlying CA local rules being nonlinear. The smallest nonlinear S-boxes emerges for d=6, but their nonlinearity is still too low to be used in practice. Nonetheless, we unearth an interesting structure of linear OCA S-boxes, proving that their Linear Components Space is itself the image of a linear CA, or equivalently a polynomial code. We finally classify all linear OCA S-boxes in terms of their generator polynomials.</p

Neural network-based double encrption for JPEG2000 images

The JPEG2000 is the more efficient next generation coding standard than the current JPEG standard.It can code files witless visual loss, and the file format is less likely to be affected by system file or bit errors.On the encryption side, the current 128-bit image encryption schemes are reported to be vulnerable to brute force. So there is a need for stronger schemes that not only utilize the efficient coding structure of the JPEG2000, but also apply stronger encryption with better key management.This research investigated a two-layer 256-bit encryption technique proposed for the JPEG2000 compatible images.In the first step, the technique used a multilayer neural network with a 128-bit key to generate single layer encrypted sequences. The second step used a cellular neural network with a different 128-bit key to finally generate a two-layer encrypted image. The projected advantages were compatible with the JPEG2000, 256-bit long key, managing each 128-bit key at separate physical locations, and flexible to opt for a single or a two-layer encryption. In order to test the proposed encryption technique for robustness, randomness tests on random sequences, correlation and histogram tests on encrypted images were conducted.The results show that random sequences pass the NIST statistical tests and the 0/1 balancedness test; the bit sequences are decorrelated, and the histogram of the resulting encrypted images is fairly uniform with the statistical properties of those of the white noise