Linear cryptanalysis method for DES cipher
EUROCRYPT '93 Workshop on the theory and application of cryptographic techniques on Advances in cryptology
Practical genetic algorithms
Handbook of Applied Cryptography
Handbook of Applied Cryptography
Evolutionary Heuristics for Finding Cryptographically Strong S-Boxes
ICICS '99 Proceedings of the Second International Conference on Information and Communication Security
Differential Cryptanalysis of DES-like Cryptosystems
CRYPTO '90 Proceedings of the 10th Annual International Cryptology Conference on Advances in Cryptology
The design of S-boxes by simulated annealing
New Generation Computing - Evolutionary computation
Estimation of average switching activity in combinational logic circuits using symbolic simulation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
FKM: a fingerprint-based key management protocol for SoC-based sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
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S-boxes constitute a cornerstone component in symmetrickey cryptographic algorithms, such as DES and AES encryption systems. In block ciphers, they are typically used to obscure the relationship between the plaintext and the ciphertext. Non-linear and noncorrelated S-boxes are the most secure against linear and differential cryptanalysis. In this paper, we focus on a two-fold objective: first, we evolve regular an S-box with high non-linearity and low auto-correlation properties using evolutionary computation; then automatically generate evolvable hardware for the obtained S-box. Targeting the former, we use the Nash equilibrium-based multi-objective evolutionary algorithm to optimise regularity, non-linearity and auto- correlation, which constitute the three main desired properties in resilient S-boxes. Pursuing the latter, we exploit genetic programming to automatically generate the evolvable hardware designs of substitution boxes that minimise hardware space, encryption/decryption time and dissipated power, which form the three main hardware characteristics. We compare our results against existing and well-known designs, which were produced by using conventional methods as well as through evolution.