Secure Hash Algorithm based on Efficient Chaotic Neural Network

Abdoun, Nabil; Assad, Safwan El; Taha, Muhammad; Assaf, Rima; Déforges, Olivier; Khalil, Mohamad

doi:10.1109/iccomm.2016.7528304

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…In 2014, Teh et al, [35] introduced a parallel chaotic hash function based on the shuffle-exchange network that runs in parallel to improve hashing speed. In 2015, Abdoun et al, [36,37] proposed a new efficient structure that consists of two parts: an efficient chaotic generator and a three or two-layer neural network. Chenaghlu et al, [38] published a new keyed parallel hashing scheme based on a new hyper sensitive chaotic system with compression ability.…”

Section: Introductionmentioning

confidence: 99%

Design and security analysis of two robust keyed hash functions based on chaotic neural networks

Abdoun

Assad

Déforges

et al. 2019

J Ambient Intell Human Comput

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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.

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Section: Introductionmentioning

confidence: 99%

Design and security analysis of two robust keyed hash functions based on chaotic neural networks

Abdoun

Assad

Déforges

et al. 2019

J Ambient Intell Human Comput

Self Cite

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“…A hash function [20] is a method for characterizing information, a data. By having a sequence of reproducible treatments at an input, it generates a fingerprint to identify the initial data.…”

Section: Hash Functionmentioning

confidence: 99%

Towards a New Algorithm to Optimize IPv6 Neighbor Discovery Security for Small Objects Networks

Ksimi

Leghris

2018

Security and Communication Networks

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In order to verify the uniqueness of link-local or unicast addresses, nodes must perform a Duplicate Address Detection process before using them. However, this process is subject to many attacks and the security is willing to be the most important issues in Small Object Networks with IPv6. In this paper, we developed a new algorithm to optimize the security in IPv6-DAD process; this method is based on SHA-512 to verify the identity of the Neighbor Discovery messages transmitted in the link local. First, before sending the NS message, the new node uses the function SHA-512 to hash to the target address and use the last 64 bits in a new field and then encrypt the result with its private key. When receiving the secure message, the existing nodes decrypt it. Our algorithm is going to secure the DAD process by using a digital signature. Overall, this algorithm showed a significant effect in terms of the Address Configuration Success Probability (ACSP).

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“…Many applications and protocols require these forms of security. However, so far, these properties have been designed separately [1][2][3].…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

Authenticated Encryption Based on Chaotic Neural Networks and Duplex Construction

Abdoun¹,

Assad²,

Hoang

et al. 2021

Symmetry

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In this paper, we propose, implement and analyze an Authenticated Encryption with Associated Data Scheme (AEADS) based on the Modified Duplex Construction (MDC) that contains a chaotic compression function (CCF) based on our chaotic neural network revised (CNNR). Unlike the standard duplex construction (SDC), in the MDC there are two phases: the initialization phase and the duplexing phase, each contain a CNNR formed by a neural network with single layer, and followed by a set of non-linear functions. The MDC is implemented with two variants of width, i.e., 512 and 1024 bits. We tested our proposed scheme against the different cryptanalytic attacks. In fact, we evaluated the key and the message sensitivity, the collision resistance analysis and the diffusion effect. Additionally, we tested our proposed AEADS using the different statistical tests such as NIST, Histogram, chi-square, entropy, and correlation analysis. The experimental results obtained on the security performance of the proposed AEADS system are notable and the proposed system can then be used to protect data and authenticate their sources.

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Secure Hash Algorithm based on Efficient Chaotic Neural Network

Cited by 14 publications

References 7 publications

Design and security analysis of two robust keyed hash functions based on chaotic neural networks

Design and security analysis of two robust keyed hash functions based on chaotic neural networks

Towards a New Algorithm to Optimize IPv6 Neighbor Discovery Security for Small Objects Networks

Authenticated Encryption Based on Chaotic Neural Networks and Duplex Construction

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