A Cryptographic Scheme for Construction of Substitution Boxes Using Quantic Fractional Transformation

Mahboob, Abid; Asif, Muhammad; Nadeem, Muhammad; Saleem, Aysha; Eldin, Sayed M.; Siddique, Imran

doi:10.1109/access.2022.3230141

Cited by 17 publications

(15 citation statements)

References 39 publications

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“…To detect the level of robustness in S-boxes, various S-boxes are evaluated, including those based on the frequency domain, transformbased maps, and chaotic maps. To generate a complete data set, a variety of existing S-boxes are considered and the characteristic values are extracted from those S-boxes [10][11][12][13][52][53][54][55][56][57]. A few of them are given in Tables 2 and 3.…”

Section: Dataset Used In the Proposed Workmentioning

confidence: 99%

Lightweight Image Encryption Scheme for IoT Environment and MachineLearning-Driven Robust S-box Selection

Mehmood,

Shafique,

Khan

et al. 2024

Preprint

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The Substitution Box (S-Box) plays a critical role in several block cipher cryptosystems due to its ability to provide essential properties of non-linearity and confusion. Therefore, incorporating a strong S-Box is critical for ensuring a high level of security and optimal performance of block cipher algorithms. In recent years, a variety of S-boxes have been developed, which fall into different robustness categories, such as high, medium, or low. Examining the robustness of an S-box to enhance cryptosystems in terms of security is a challenging task. Although parameters such as Bit Independence Criterion (BIC), Nonlinearity (NL), Strict Avalanche Criterion (SAC), Linear Approximation Probability (LP), and Differential Uniformity (DU) can be useful to determine the robustness levels, manually evaluating each S-box is an ineffective and time-consuming approach. To overcome this challenge, a machine learning model is developed, utilizing the parameters that evaluate the strength of the S-box as features. Additionally, a novel lightweight image encryption scheme suited for IoT applications is also proposed, incorporating various S-boxes. Implementing robust S-boxes also strengthens the security of the proposed encryption scheme. However, to enhance its security further, four distinct cryptographic techniques such as chaotic maps, Discrete Wavelet Transform (DWT), Substitution Box (S-box), and Dynamic Random Phase Encoding (DRPE) are employed. To evaluate the efficiency of the proposed encryption scheme in terms of statistical and visual analysis, numerous parameters are considered, including entropy, correlation, contrast, energy, noise and cropping attack analysis, histogram analysis, correlation, entropy, differential analysis, occlusion attack, noise attack, and speed performance analysis. Moreover, a detailed comparison is performed between the proposed and existing encryption schemes to demonstrate that the proposed approach is more efficient than existing ones.

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Section: Dataset Used In the Proposed Workmentioning

confidence: 99%

Lightweight Image Encryption Scheme for IoT Environment and MachineLearning-Driven Robust S-box Selection

Mehmood,

Shafique,

Khan

et al. 2024

Preprint

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“…The lower energy value, on the other hand, indicates an irregular texture pattern and expresses the effect of the efficient encryption algorithm 46 – 48 The mathematical equation to compute energy is Energy=∑ijp(i,j)2…”

Section: Experimental Analysis Of the Proposed Algorithmmentioning

confidence: 99%

Hybrid image encryption algorithm based on compressive sensing, gray wolf optimization, and chaos

Abdul-Kareem,

Mahmoud Al-Jawher

2023

J. Electron. Imag.

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Growing reliance on digital communications has necessitated development of dependable and secure technologies to ensure that the transmission and reception of images over the Internet do not pose a risk to the data of individuals and governments. We propose developing a hybrid image encryption and compression algorithm by combining compressive sensing, the gray wolf algorithm, and multidimensional chaotic systems. It aims to generate a highly secure encrypted image while conserving transmission and storage resources. This algorithm overlaps several stages designed to protect vital images while minimizing size. First, the image is converted to the frequency domain using the discrete wavelet transform. Then, the discrete wavelet transform coefficients are scrambled globally using the Waleed-Ali Map and the gray wolf algorithm. Second, the confused image is measured by a parameters-controlled matrix to reduce transmission costs. The final encrypted image is obtained after performing the diffusion operation with a bitstream derived from the Nahrain chaotic map. The average peak signal-to-noise ratio score was 53.1995, and the average mean squared error score was 0.6130, demonstrating that the plaintext and decrypted images are identical. The average correlation coefficient score was −0.010095; the average entropy analysis was 7.9987; and the average number of pixel change rate and unified average changing intensity analyses were 99.60 and 33.52, respectively. The experimental results demonstrate the algorithm's efficiency and robustness, as well as the high quality of the reconstructed image.

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“… S-boxes Contrast Energy Homogeneity Entropy 59 10.4087 0.0157 0.3813 7.9970 14 10.3977 0.0157 0.3839 7.9974 60 10.5391 0.0157 0.3833 7.9971 61 10.4441 0.0157 0.3994 7.9973 62 10.6010 0.0157 0.3958 7.9967 15 10.5229 0.0157 0.3849 7.9974 63 10.3853 0.0157 0.3994 7.9971 Proposed 10.4775 0.01571 0.3810 7.9972 …”

Section: Image Encryptionmentioning

confidence: 99%

A study of text-theoretical approach to S-box construction with image encryption applications

Mahboob,

Nadeem,

Rasheed

2023

Sci Rep

Self Cite

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Data protection is regarded as one of the biggest issues facing companies that have been using public data for a long time. Numerous encryption techniques have been used to address these issues and safeguard data from any malicious attempts and assaults. A substitution box (S-box) is the basic component for modern block ciphers, which helps to ensure robust security of plain data while encryption and permits its lawful decipherment. The goal of this paper is to recommend an effective, original, and straightforward technique for the creation of robust S-boxes. A sample S-box is generated in the proposed work using the word “UNITY” but other words can also be used to generate many powerful S-boxes. The ASCII code is used to translate the word “UNITY” into binary form, after which a distinct matrix is constructed for each character of the word. In the next phase, a linear fractional transformation is constructed using these matrices, which is then utilized to generate the S-box. The constructed S-box was then evaluated against typical security standards to support its high cryptographic authority. The generated S-box's statistical and algebraic resilience is demonstrated by its very low linear probability and differential probability scores of 0.125 and 0.039, respectively, and a high non-linearity score of 111.5. To evaluate the effectiveness of the image encryption scheme, digital images are encoded using the created S-box. The performance and comparative research demonstrate that the suggested S-box is a real candidate for applications in the field of image encryption and has a stronger performance base.

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A Cryptographic Scheme for Construction of Substitution Boxes Using Quantic Fractional Transformation

Cited by 17 publications

References 39 publications

Lightweight Image Encryption Scheme for IoT Environment and MachineLearning-Driven Robust S-box Selection

Lightweight Image Encryption Scheme for IoT Environment and MachineLearning-Driven Robust S-box Selection

Hybrid image encryption algorithm based on compressive sensing, gray wolf optimization, and chaos

A study of text-theoretical approach to S-box construction with image encryption applications

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