The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

Predicting Non-linear Cellular Automata Quickly by Decomposing Them into Linear Ones

We show that a wide variety of non-linear cellular automata (CAs) can be decomposed into a quasidirect product of linear ones. These CAs can be predicted by parallel circuits of depth O(log^2 t) using gates with binary inputs, or O(log t) depth if ``sum mod p'' gates with an unbounded number of inputs are allowed. Thus these CAs can be predicted by (idealized) parallel computers much faster than by explicit simulation, even though they are non-linear. This class includes any CA whose rule, when written as an algebra, is a solvable group. We also show that CAs based on nilpotent groups can be predicted in depth O(log t) or O(1) by circuits with binary or ``sum mod p'' gates respectively. We use these techniques to give an efficient algorithm for a CA rule which, like elementary CA rule 18, has diffusing defects that annihilate in pairs. This can be used to predict the motion of defects in rule 18 in O(log^2 t) parallel time

Finding an Effective Metric Used for Bijective S-Box Generation by Genetic Algorithms

In cryptography, S-box is a basic component of symmetric key algorithms which performs nonlinear substitution. S-boxes need to be highly nonlinear, so that the cipher can resist linear cryptanalysis. The main criteria for cryptographically strong (n × n) S-box are: • High non linearity; • High algebraic degree; • Balanced structure; • Good auto correlation properties. Our task was to give some suggestions for finding an effective metric used for generation bijective optimal S-Box. Because of the given problem’s complexity, our group considered different approaches and we gave a few suggestions for problem solving

Cryptography Using Quasi Group and Chaotic Maps

In this paper a symmetric key (stream cipher mode/block cipher mode) cryptosystem is proposed, involving chaotic maps and quasi group. The proposed cryptosystem destroys any existing patterns in the input, and also, it maximizes entropy. Moreover, the n-grams illustrate that the proposed cryptosystem is secure against the statistics analysis. Furthermore, Experimental results show that the ciphertext has good diffusion and confusion properties with respect to the plaintext and the key, also the results demonstrate that the block cipher mode gives higher entropy than the steam cipher mode

The Quasigroup Block Cipher and its Analysis

This thesis discusses the Quasigroup Block Cipher (QGBC) and its analysis. We first present the basic form of the QGBC and then follow with improvements in memory consumption and security. As a means of analyzing the system, we utilize tools such as the NIST Statistical Test Suite, auto and crosscorrelation, then linear and algebraic cryptanalysis. Finally, as we review the results of these analyses, we propose improvements and suggest an algorithm suitable for low-cost FPGA implementation