In this paper, we propose, implement, and analyze the structures of two keyed hashfunctions using the Chaotic Neural Network (CNN). These structures are based on Spongeconstruction, and they produce two variants of hash value lengths, i.e., 256 and 512 bits. The firststructure is composed of two-layered CNN, while the second one is formed by one-layered CNN anda combination of nonlinear functions. Indeed, the proposed structures employ two strong nonlinearsystems, precisely a chaotic system and a neural network system. In addition, the proposed study isa new methodology of combining chaotic neural networks and Sponge construction that is provedsecure against known attacks. The performance of the two proposed structures is analyzed in termsof security and speed. For the security measures, the number of hits of the two proposed structuresdoesn’t exceed 2 for 256-bit hash values and does not exceed 3 for 512-bit hash values. In terms ofspeed, the average number of cycles to hash one data byte (NCpB) is equal to 50.30 for Structure 1,and 21.21 and 24.56 for Structure 2 with 8 and 24 rounds, respectively. In addition, the performance ofthe two proposed structures is compared with that of the standard hash functions SHA-3, SHA-2, andwith other classical chaos-based hash functions in the literature. The results of cryptanalytic analysisand the statistical tests highlight the robustness of the proposed keyed hash functions. It also showsthe suitability of the proposed hash functions for the application such as Message Authentication,Data Integrity, Digital Signature, and Authenticated Encryption with Associated Data.

No abstract

In this paper, we designed, implemented, and analyzed the performance, in terms of security and speed, of two proposed keyed Chaotic Neural Network (CNN) hash functions based on Merkle-Dåmgard (MD) construction with three output schemes: CNN-Matyas-Meyer-Oseas, Modified CNN-Matyas-Meyer-Oseas, and CNN-Miyaguchi-Preneel. The first hash function's structure is composed of two-layer chaotic neural network while the structure of the second hash function is formed of one-layer chaotic neural network followed by non-linear layer functions. The obtained results of several statistical tests and cryptanalytic analysis highlight the robustness of the proposed keyed CNN hash functions, which is fundamentally due to the strong non-linearity of both the chaotic systems and the neural networks. The comparison of the performance analysis with some chaosbased hash functions of the literature and with standard hash functions make the proposed hash functions suitable for data integrity, message authentication, and digital signature applications.

No abstract

No abstract