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Title: A genetic algorithm for computing the k-error linear complexity of cryptographic sequences
Authors: Alecu, Alexandra
Salagean, A.M.
Issue Date: 2007
Publisher: © IEEE
Citation: ALECU, A. and SALAGEAN, A.M., 2007. A genetic algorithm for computing the k-error linear complexity of cryptographic sequences. Proceedings of IEEE Congress on Evolutionary Computation, 25-28 September, Singapore, pp. 3569-3576
Abstract: Some cryptographical applications use pseudorandom sequences and require that the sequences are secure in the sense that they cannot be recovered by only knowing a small amount of consecutive terms. Such sequences should therefore have a large linear complexity and also a large k-error linear complexity. Efficient algorithms for computing the kerror linear complexity of a sequence over a finite field only exist for sequences of period equal to a power of the characteristic of the field. It is therefore useful to find a general and efficient algorithm to compute a good approximation of the k-error linear complexity. In this paper we investigate the design of a genetic algorithm to approximate the k-error linear complexity of a sequence. Our preliminary experiments show that the genetic algorithm approach is suitable to the problem and that a good scheme would use a medium sized population, an elitist type of selection, a special design of the two point random crossover and a standard random mutation. The algorithm outputs an approximative value of the k-error linear complexity which is on average only 19.5% higher than the exact value. This paper intends to be a proof of concept that the genetic algorithm technique is suitable for the problem in hand and future research will further refine the choice of parameters.
Description: This article was published in the Proceedings of IEEE Congress on Evolutionary Computation 2007 [© IEEE] and is also available at: http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=4424445&conhome=1000284 Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
URI: https://dspace.lboro.ac.uk/2134/3205
Appears in Collections:Conference Papers and Presentations (Computer Science)

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