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A genetic algorithm for computing the k-error linear complexity of cryptographic sequences
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.
History
School
- Science
Department
- Computer Science
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. 3569-3576Publisher
© IEEEPublication date
2007Notes
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.Language
- en