A New Lightweight Stream Cipher Based on Chaos

Ding, Ling; Liu, Chunyuan; Zhang, Yanpeng; Ding, Qun

doi:10.3390/sym11070853

Cited by 40 publications

(26 citation statements)

References 40 publications

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“…In a similar way, the authors in [19] showed the effects of time delay on burst synchronization transitions of a neural network which was locally modeled by Hindmarsh-Rose neurons. On the one hand, the main drawback of those works was the lack of statistical tests according to the National Institute of Standard and Technology (NIST), as done in other chaotic systems given in [20][21][22], to guarantee the randomness of the chaotic sequences. On the other hand, and in addition to NIST tests, the authors in [23] recommend to improve the key space when using chaotic maps, thus enhancing the image encryption schemes.…”

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confidence: 99%

Chaotic Image Encryption Using Hopfield and Hindmarsh–Rose Neurons Implemented on FPGA

Tlelo‐Cuautle

Díaz-Muñoz

González-Zapata

et al. 2020

Sensors

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Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the Hindmarsh-Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan-Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh-Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.In [2] J.J. Hopfield introduced the neuron model that nowadays is known as the Hopfield neural network. Ten years later, a modified model of Hopfield neural network was proposed in [3], and applied in information processing. Immediately, the Hopfield neural network was adapted to generate chaotic behavior in [4] where the authors explored bifurcation diagrams. In [5] the simplified Hopfield neuron model was designed to use a sigmoid as activation function, and three neurons were used to generate chaotic behavior. In addition, the authors performed an optimization process updating the weights of the neurons interconnections. The Hopfield neuron was combined with a chaotic map in [6] to be applied in chaotic masking. More recently, the authors in [7] proposed an image encryption algorithm using the Hopfield neural network. In the same direction, the authors in [8] detailed the behavior of Hindmarsh-Rose neuron to generate chaotic behavior. Its bifurcation diagrams were described in [9], and the results were used to select the values of the model to improve chaotic behavior. Hindmarsh-Rose neurons were synchronized in [10], optimizing the scheme of Lyapunov function with two gain coefficients. In this way, the synchronization region is estimated by evaluating the Lyapunov stability. Two Hindmarsh-Rose neurons were synchronized in [11], and the system was used to mask information in continuous time. To show that the neurons generate chaotic behavior, one must compute Lyapunov exponents, and for the Hindmarsh-Rose neuron they were evaluated by the TISEAN package in [12].The Hopfield neural network has been widely applied in chaotic systems [13][14][15]. This network consists of three neurons, and the authors in [13] proposed a simplified model by removing the synaptic weight connection of the third and second neuron in the original Hopfield network. Numerical simulations were carried out considering values from t...

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confidence: 99%

Chaotic Image Encryption Using Hopfield and Hindmarsh–Rose Neurons Implemented on FPGA

Tlelo‐Cuautle

Díaz-Muñoz

González-Zapata

et al. 2020

Sensors

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“…These outputs have been converted to random number sequences with the help of various conversion functions. There are many suggestions in this design approach that use different chaotic systems and different conversion functions [20][21][22][23]. Electronic circuit simulations of chaotic systems are generally used in chaos based TRNG designs.…”

Section: Chaos and Randomnessmentioning

confidence: 99%

A Method to Determine the Most Suitable Initial Conditions of Chaotic Map in Statistical Randomness Applications

Açikkapi

Özkaynak

2021

IEEE Access

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“…It has been suggested that different entropy sources can be produced by using different initial conditions and control parameters, as they will produce different outputs with small changes that may occur in the initial conditions and control parameters. Many cryptographic protocols such as image encryption algorithms [10,11], key generators [12,13] and s-box designs [14] have been proposed using this design idea, as visualized in Figure 1. Although this design approach has been widely studied, the security analysis of some proposals has not been done according to certain criteria, which has caused various security problems.…”

Section: Chaos-based S-box Structuresmentioning

confidence: 99%

A Novel Method for Performance Improvement of Chaos-Based Substitution Boxes

Artuğer

Özkaynak

2020

Symmetry

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Symmetry plays an important role in nonlinear system theory. In particular, it offers several methods by which to understand and model the chaotic behavior of mathematical, physical and biological systems. This study examines chaotic behavior in the field of information security. A novel method is proposed to improve the performance of chaos-based substitution box structures. Substitution box structures have a special role in block cipher algorithms, since they are the only nonlinear components in substitution permutation network architectures. However, the substitution box structures used in modern block encryption algorithms contain various vulnerabilities to side-channel attacks. Recent studies have shown that chaos-based designs can offer a variety of opportunities to prevent side-channel attacks. However, the problem of chaos-based designs is that substitution box performance criteria are worse than designs based on mathematical transformation. In this study, a postprocessing algorithm is proposed to improve the performance of chaos-based designs. The analysis results show that the proposed method can improve the performance criteria. The importance of these results is that chaos-based designs may offer opportunities for other practical applications in addition to the prevention of side-channel attacks.

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A New Lightweight Stream Cipher Based on Chaos

Cited by 40 publications

References 40 publications

Chaotic Image Encryption Using Hopfield and Hindmarsh–Rose Neurons Implemented on FPGA

Chaotic Image Encryption Using Hopfield and Hindmarsh–Rose Neurons Implemented on FPGA

A Method to Determine the Most Suitable Initial Conditions of Chaotic Map in Statistical Randomness Applications

A Novel Method for Performance Improvement of Chaos-Based Substitution Boxes

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