Image encryption using a combination of Grain‐128a algorithm and Zaslavsky chaotic map

Balaska, Nawel; Ahmida, Zahir; Belmeguenaï, Aïssa; Boumerdassi, Selma

doi:10.1049/iet-ipr.2019.0671

Cited by 17 publications

(3 citation statements)

References 35 publications

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“…Balaska et al [8] merged the Grain-128a stream cipher technique with a 2D Zaslavsky chaotic map to increase sensitivity and security in the initial parameter selection. Then, to generate the chaotic map's needed parameters, a 256-bit secret key with a fixed length is used.…”

Section: Related Workmentioning

confidence: 99%

Medical image encryption using multi chaotic maps

Hussain¹,

Khodher

2023

TELKOMNIKA

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Over the last twenty years, chaos-based encryption has been an increasingly popular way to encrypt and decrypt data using nonlinear dynamics and deterministic chaos. Discrete chaotic systems based on iterative maps have gotten a lot of interest because of their simplicity and speed. In this paper, three kinds of chaotic maps are utilized to build a digital image encryption strategy depending on a chaotic system. These chaotic maps are the logistic map, Arnold Cat's map, and Baker's map. In addition to using the triple data encryption standard (3DES) encryption scheme with the chaotic maps mentioned. The results of the experiments revealed that the suggested digital image encryption technique is both efficient and secure, making it ideal for usage in insecure networks. The transmission control protocol (TCP)/internet protocol (IP) protocol was used for the purpose of transferring data from server to client through the network and vice versa.

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Section: Related Workmentioning

confidence: 99%

Medical image encryption using multi chaotic maps

Hussain¹,

Khodher

2023

TELKOMNIKA

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“…Coupled Eq. ( 1) [17] is used to define the 2-D map: (1) Initial values are , and , and , , and are the regulating parameters used to monitor the predicted chaotic behavior, while e is the exponentiation [17], [18]. When the parameters r = 3.0, v = 400/3, and ε = 0.3 are set of the parameters, this map shows chaotic behavior [19].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Key Generation Using GWO for IoT System

A. Hameedi,

A. Hatem,

N. Hasoon

2024

JOIV : Int. J. Inform. Visualization

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One well-known technological advancement that significantly impacts many things is the Internet of Things (IoT). These include connectivity, work, healthcare, and the economy. IoT can improve life in many situations, including classrooms and smart cities, through work automation, increased output, and decreased worry. However, cyberattacks and other risks significantly impact intelligent Internet of Things applications. Key generation is essential in information security and the various applications that use a distributed system, networks, or Internet of Things (IoT) systems. Several algorithms have been developed to protect IoT applications from malicious attacks; since IoT devices usually have small memory resources and limited computing and power resources, traditional key generation methods are inappropriate because they require high computational power and memory usage. This paper proposes a method of Dynamic Key Generation Method (DKGM) to overcome the difficulty using a specific chaotic map called the Zaslavskii Map and a swarm intelligent algorithm for optimization called Grey Wolf Optimizer (GWO). DKGM's ability to generate several groups-seed numbers using the Zaslavskii map depends on various initial parameters. GWO selects strong generated numbers depending on the randomness test as a fitness function. Three wolfs GWα, GWβ, and GWΩ, are used to simulate the behavior of a pack of grey wolves when attacking prey. The speed and position of each wolf are updated depending on the best three wolves. Finally, use the sets GWα in the round, GWβ in the subkey, and GWΩ in shifting operations of the Chacha20 hash function. The dynamic procedure was used to improve the high-security analysis of the DKGM approach over earlier methods. Simulations show that the suggested method is preferable for IoT applications.

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“…Finally, the cipher image is obtained by random diffusion. In (14), proposes an image encryption using a combination of Grain-128a algorithm and Zaslavsky chaotic map; where a process of bit confusion and diffusion is applied using a sequences generated by the chaotic map. Despite the advantages of algorithms mentioned above, they have a number of inherent limitations.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Image Encryption Using a Dynamic, Nonlinear and Secret Diffusion Scheme

et al. 2021

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This paper presents a new secret diffusion scheme called Round Key Permutation (RKP) based on the nonlinear, dynamic and pseudorandom permutation for encrypting images by block, since images are considered particular data because of their size and their information, which are two-dimensional nature and characterized by high redundancy and strong correlation. Firstly, the permutation table is calculated according to the master key and sub-keys. Secondly, scrambling pixels for each block to be encrypted will be done according the permutation table. Thereafter the AES encryption algorithm is used in the proposed cryptosystem by replacing the linear permutation of ShiftRows step with the nonlinear and secret permutation of RKP scheme; this change makes the encryption system depend on the secret key and allows both to respect the second Shannon's theory and the Kerckhoff principle. Security analysis of cryptosystem demonstrates that the proposed diffusion scheme of RKP enhances the fortress of encryption algorithm, as can be observed in the entropy and other obtained values.

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Image encryption using a combination of Grain‐128a algorithm and Zaslavsky chaotic map

Cited by 17 publications

References 35 publications

Medical image encryption using multi chaotic maps

Medical image encryption using multi chaotic maps

Dynamic Key Generation Using GWO for IoT System

An Efficient Image Encryption Using a Dynamic, Nonlinear and Secret Diffusion Scheme

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