Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography

被引:32
|
作者
Picek, Stjepan [1 ,2 ,3 ]
Carlet, Claude [4 ,5 ]
Guilley, Sylvain [6 ,7 ]
Miller, Julian F. [8 ]
Jakobovic, Domagoj [9 ]
机构
[1] Katholieke Univ Leuven, ESAT COSIC, Kasteelpk Arenberg 10,Bus 2452, B-3001 Leuven Heverlee, Belgium
[2] Katholieke Univ Leuven, iMinds, Kasteelpk Arenberg 10,Bus 2452, B-3001 Leuven Heverlee, Belgium
[3] Univ Paris 08, UMR 7539, CNRS, LAGA, F-93526 St Denis 02, France
[4] Univ Paris 13, CNRS, UMR 7539, LAGA, F-93430 Villetaneuse, France
[5] Univ Paris 08, F-93526 St Denis 02, France
[6] TELECOM ParisTech, Paris, France
[7] Secure IC SAS, Rennes, France
[8] Univ York, Dept Elect, York, N Yorkshire, England
[9] Univ Zagreb, Fac Elect Engn & Comp, Zagreb 41000, Croatia
关键词
Evolutionary algorithms; Boolean functions; cryptography; comparison; FAST ALGEBRAIC ATTACKS; STREAM CIPHERS; DESIGN;
D O I
10.1162/EVCO_a_00190
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.
引用
收藏
页码:667 / 694
页数:28
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