scholarly journals Authenticated Encryption Based on Chaotic Neural Networks and Duplex Construction

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2432
Author(s):  
Nabil Abdoun ◽  
Safwan El Assad ◽  
Thang Manh Hoang ◽  
Olivier Deforges ◽  
Rima Assaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Statistical Tests ◽  
Single Layer ◽  
Linear Functions ◽  
Authenticated Encryption ◽  
Chaotic Neural Network ◽  
Chi Square ◽  
Chaotic Neural Networks ◽  
Two Phases ◽  
Associated Data

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.

scholarly journals Designing Two Secure Keyed Hash Functions Based on Sponge Construction and the Chaotic Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1012 ◽  
Author(s):  
Nabil Abdoun ◽  
Safwan El Assad ◽  
Thang Manh Hoang ◽  
Olivier Deforges ◽  
Rima Assaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Statistical Tests ◽  
Hash Functions ◽  
Message Authentication ◽  
Security Measures ◽  
Chaotic Neural Network ◽  
Chaotic Neural Networks ◽  
Classical Chaos ◽  
Neural Network System ◽  
Number Of Cycles

In this paper, we propose, implement, and analyze the structures of two keyed hash functions using the Chaotic Neural Network (CNN). These structures are based on Sponge construction, and they produce two variants of hash value lengths, i.e., 256 and 512 bits. The first structure is composed of two-layered CNN, while the second one is formed by one-layered CNN and a combination of nonlinear functions. Indeed, the proposed structures employ two strong nonlinear systems, precisely a chaotic system and a neural network system. In addition, the proposed study is a new methodology of combining chaotic neural networks and Sponge construction that is proved secure against known attacks. The performance of the two proposed structures is analyzed in terms of security and speed. For the security measures, the number of hits of the two proposed structures doesn’t exceed 2 for 256-bit hash values and does not exceed 3 for 512-bit hash values. In terms of speed, 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 of the two proposed structures is compared with that of the standard hash functions SHA-3, SHA-2, and with other classical chaos-based hash functions in the literature. The results of cryptanalytic analysis and the statistical tests highlight the robustness of the proposed keyed hash functions. It also shows the suitability of the proposed hash functions for the application such as Message Authentication, Data Integrity, Digital Signature, and Authenticated Encryption with Associated Data.

CHAOTIC BURSTS AND BIFURCATION IN CHAOTIC NEURAL NETWORKS WITH RING STRUCTURE

2001 ◽  
Vol 11 (06) ◽  
pp. 1631-1643 ◽  
Author(s):  
HIROYUKI KITAJIMA ◽  
TETSUYA YOSHINAGA ◽  
KAZUYUKI AIHARA ◽  
HIROSHI KAWAKAMI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ring Structure ◽  
Closed Curve ◽  
Burst Firing ◽  
Neural Systems ◽  
Global Dynamics ◽  
Chaotic Neural Network ◽  
Chaotic Neural Networks ◽  
Functional Roles

We investigate a noninvertible map describing burst firing in a chaotic neural network model with ring structure. Since each neuron interacts with many other neurons in biological neural systems, it is important to consider global dynamics of networks composed of nonlinear neurons in order to clarify not only mechanisms of emergence of the burst firing but also its possible functional roles. We analyze parameter regions in which burst firing can be observed, and show that dynamics of strange attractors with burst firing is related to the generation of a homoclinic-like situation and vanishing of an invariant closed curve of the map.

Research on Railway Engineering Cost Prediction Model Based on Chaotic Neural Networks and CS

2011 ◽  
Vol 204-210 ◽  
pp. 1291-1294
Author(s):  
Yan Chun Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influence Factors ◽  
Chaotic Neural Network ◽  
Short Term ◽  
Chaotic Neural Networks ◽  
Improve Accuracy ◽  
Railway Engineering ◽  
The Cost ◽  
Engineering Cost

It is always hard to draw on the experience of completed projects to predict engineering cost, and the nonlinear characteristic of the influence factors of engineering cost increases the difficulty of prediction. Less efforts and higher accuracy are the objects pursued by related researchers. In this paper, the Cost Significant theorem is applied to simplify computing and the chaotic neural network is used to improve accuracy. The prediction model is rooted from the nonlinear dynamic chaotic system theory and two techniques employed are phase space reconstruction and chaotic neural network construction. The experiment results indicate that the model is suitable for estimating short-term engineering investment and the prediction accuracy is improved.

A FLEXIBLE ANNEALING CHAOTIC NEURAL NETWORK TO MAXIMUM CLIQUE PROBLEM

2007 ◽  
Vol 17 (03) ◽  
pp. 183-192 ◽  
Author(s):  
GANG YANG ◽  
ZHENG TANG ◽  
ZHIQIANG ZHANG ◽  
YUNYI ZHU
Keyword(s):  
Neural Network ◽  
Chaotic Dynamics ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Maximum Clique ◽  
Maximum Clique Problem ◽  
Stable States ◽  
Chaotic Neural Network ◽  
Chaotic Neural Networks ◽  
Clique Problem

Based on the analysis and comparison of several annealing strategies, we present a flexible annealing chaotic neural network which has flexible controlling ability and quick convergence rate to optimization problem. The proposed network has rich and adjustable chaotic dynamics at the beginning, and then can converge quickly to stable states. We test the network on the maximum clique problem by some graphs of the DIMACS clique instances, p-random and k random graphs. The simulations show that the flexible annealing chaotic neural network can get satisfactory solutions at very little time and few steps. The comparison between our proposed network and other chaotic neural networks denotes that the proposed network has superior executive efficiency and better ability to get optimal or near-optimal solution.

Control and Monitor Model of the Critical State of Corporate Strategic Decision Making Based on the Chaotic Neural Networks

2013 ◽  
Vol 567 ◽  
pp. 101-111
Author(s):  
Wei Wang ◽  
Yan Wei Fan ◽  
Xiu Hui Qi
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Critical State ◽  
Network Control ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Neural Network Control ◽  
Chaotic Neural Network ◽  
Chaotic Neural Networks ◽  
Controlling System

Timely strategic decision-making is an important guarantee for corporate to remain invincible in the competition. This paper sorts out the current researches of the control of the strategic decision-making, proposes the processing model to control the critical state of the strategic decision making as well as the judging methods, and determines the best timing to apply the chaotic neural network control for the strategic decision making on the basis of constructing the index controlling system, so that the accurate control for the corporate strategic decision making can be achieved.

scholarly journals NEURAL NETWORK-BASED DOUBLE ENCRYPTION FOR JPEG2000 IMAGES

2017 ◽  
Author(s):  
Qurban Ali Memon
Keyword(s):  
Neural Network ◽  
Key Management ◽  
Statistical Tests ◽  
Single Layer ◽  
Cellular Neural Network ◽  
Random Sequences ◽  
Encryption Schemes ◽  
Encrypted Images ◽  
System File ◽  
Randomness Tests

The JPEG2000 is the more efficient next generation coding standard than the current JPEG standard. It can code files witless visual loss, and the file format is less likely to be affected by system file or bit errors. On the encryption side, the current 128-bit image encryption schemes are reported to be vulnerable to brute force. So there is a need for stronger schemes that not only utilize the efficient coding structure of the JPEG2000, but also apply stronger encryption with better key management. This research investigated a two-layer 256-bit encryption technique proposed for the JPEG2000 compatible images. In the first step, the technique used a multilayer neural network with a 128-bit key to generate single layer encrypted sequences. The second step used a cellular neural network with a different 128-bit key to finally generate a two-layer encrypted image. The projected advantages were compatible with the JPEG2000, 256-bit long key, managing each 128-bit key at separate physical locations, and flexible to opt for a single or a two-layer encryption. In order to test the proposed encryption technique for robustness, randomness tests on random sequences, correlation and histogram tests on encrypted images were conducted. The results show that random sequences pass the NIST statistical tests and the 0/1 balancedness test; the bit sequences are decorrelated, and the histogram of the resulting encrypted images is fairly uniform with the statistical properties of those of the white noise.  

Fuzzy Chaotic Neural Networks

2011 ◽  
pp. 520-555
Author(s):  
Tang Mo ◽  
Wang Kejun ◽  
Zhang Jianmin ◽  
Zheng Liying
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Fuzzy Number ◽  
Fuzzy Neural Networks ◽  
Dynamic Chaos ◽  
Chaotic Neural Network ◽  
Chaotic Neural Networks ◽  
Fuzzy Neural ◽  
Chaotic Neuron

An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is still obscure. Both fuzzy logic and dynamic chaos are internal features of the human brain. Therefore, to fuse artificial neural networks, fuzzy logic and dynamic chaos together to constitute fuzzy chaotic neural networks is a novel method. This chapter is focused on the new ways of fuzzy neural networks construction and its application based on the existing achievement in this field. Four types of fuzzy chaotic neural networks are introduced, namely chaotic recurrent fuzzy neural networks, cooperation fuzzy chaotic neural networks, fuzzy number chaotic neural networks and self-evolution fuzzy chaotic neural networks. Chaotic recurrent fuzzy neural networks model is developed based on existing recurrent fuzzy neural networks through introducing chaos mapping into the membership layer. As it is a dynamic system, the input of neuron not only processes the information of former monument but also contains chaos maps information which is provided by dynamic chaos. Cooperation fuzzy chaotic neural network is proposed on the basis of simplified T-S fuzzy chaotic neural networks and Aihara chaotic neuron. It realizes fuzzy reasoning process by a neural network structure in which the rule inference part is realized by chaotic neural networks. Then enlightened by fuzzy number neural networks we propose a fuzzy number chaotic neuron, which is obtained by blurring the Aihara chaotic neuron. Using these neurons to construct fuzzy number chaotic neural networks, the mathematical model and weight updating rules are also given. At last, a self-evolution fuzzy chaotic neural network is proposed according to the principle of self-evolution network, which unifies the fuzzy Hopfield neural network constitution method.

Invariant Features Tracking Based on Transiently Chaotic Neural Network for Markerless Augmented Reality

2011 ◽  
Vol 181-182 ◽  
pp. 37-42
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Neural Network ◽  
Augmented Reality ◽  
Image Matching ◽  
Feature Matching ◽  
Feature Point ◽  
Image Feature ◽  
Chaotic Neural Network ◽  
Scale Invariant ◽  
Chaotic Neural Networks ◽  
Invariant Features

Tracking and registration of camera and object is one of the most important issues in Augmented Reality (AR) systems. Markerless visual tracking technologies with image feature are used in many AR applications. Feature point based neural network image matching method has attracted considerable attention in recent years. This paper proposes an approach to feature point correspondence of image sequence based on transient chaotic neural networks. Rotation and scale invariant features are extracted from images firstly, and then transient chaotic neural network is used to perform global feature matching and perform the initialization phase of the tracking. Experimental results demonstrate the efficiency and the effectiveness of the proposed method.

scholarly journals Neural network integration of classical statistical tests for processing small samples of biometrics data

Dependability ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 22-27 ◽  
Author(s):  
A. I. Ivanov ◽  
E. N. Kuprianov ◽  
S. V. Tureev
Keyword(s):  
Neural Network ◽  
Statistical Tests ◽  
Pair Correlation ◽  
Test Sample ◽  
Small Samples ◽  
Network Integration ◽  
Chi Square ◽  
Biometric Data ◽  
Joint Application ◽  
Artificial Neurons

The Aim of this paper is to increase the power of statistical tests through their joint application to reduce the requirement for the size of the test sample. Methods. It is proposed to combine classical statistical tests, i.e. chi square, Cram r-von Mises and Shapiro-Wilk by means of using equivalent artificial neurons. Each neuron compares the input statistics with a precomputed threshold and has two output states. That allows obtaining three bits of binary output code of a network of three artificial neurons. Results. It is shown that each of such criteria on small samples of biometric data produces high values of errors of the first and second kind in the process of normality hypothesis testing. Neural network integration of three tests under consideration enables a significant reduction of the probabilities of errors of the first and second kind. The paper sets forth the results of neural network integration of pairs, as well as triples of statistical tests under consideration. Conclusions. Expected probabilities of errors of the first and second kind are predicted for neural network integrations of 10 and 30 classical statistical tests for small samples that contain 21 tests. An important element of the prediction process is the symmetrization of the problem, when the probabilities of errors of the first and second kind are made identical and averaged out. Coefficient modules of pair correlation of output states are averaged out as well by means of artificial neuron adders. Only in this case the connection between the number of integrated tests and the expected probabilities of errors of the first and second kind becomes linear in logarithmic coordinates.

ASSOCIATIVE MEMORY USING SYNCHRONIZATION IN A CHAOTIC NEURAL NETWORK

2001 ◽  
Vol 12 (01) ◽  
pp. 19-29 ◽  
Author(s):  
Z. TAN ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Storage Capacity ◽  
Initial Conditions ◽  
Neural Systems ◽  
Chaotic Neural Network ◽  
Relative Time ◽  
Chaotic Neural Networks ◽  
And Storage

Synchronization is introduced into a chaotic neural network model to discuss its associative memory. The relative time of synchronization of trajectories is used as a measure of pattern recognition by chaotic neural networks. The retrievability of memory is shown to be connected to synapses, initial conditions and storage capacity. The technique is simple and easy to apply to neural systems.

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