Identifying traffic of same keys in cryptographic communications using fuzzy decision criteria and bit-plane measures

Arvind, .; Ratan, Ram

doi:10.1007/s13198-019-00878-7

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…A pixel value is represented with 24 bits (8 bit for each colour band). for more detail on images, one may refer 1,2, 39,40 .…”

Section: Histogram Equalisation and Bit-plane Eencryptionmentioning

confidence: 99%

“…In selective bit-plane level encryption, one can choose even a single bit-plane or some bit-planes selected suitably to encrypt them. In bit-plane level encryption, lSB planes of an image are used to XoR with specific MSB planes 14 and an AES block cipher is used to encrypt chosen bit-planes 39 . In single bit-plane encryption 12.5%, in double bit-plane encryption 25%, and so on, and in encrypting all the bit-planes 100% image data is encrypted.…”

Section: Bit-plane Encryptionmentioning

confidence: 99%

“…The values of different measures given in Table 1-4 exhibits that the bit-plane measures are the suitable measures to form features for the classification of the images and the bit-planes. from these features, the membership values are computed for a given pattern based on the value of features for the reference pattern and membership function [39][40] . let An image belongs to a class rc for which the similarity score rc S for feature vector of unknown image concerning the feature vector of the reference image is maximum 40 .…”

Section: Identification Of He Images Andmentioning

confidence: 99%

“…In the cryptanalysis, an attacker exploits the characteristics of data such as the occurrence of gray levels, patterns, and the neighbourhood similarity characteristics of image pixels; key characteristics such as the related keys, weak keys, and equivalent keys; and the vulnerabilities if any in the encryption algorithm. The cryptanalysis of some of the image ciphers is reported [31][32][33][34][35][36][37][38][39][40][41] where it was shown that the reported security solutions were not secure as claimed and the attackers can obtain the meaningful image details, key information from the encrypted images. The cryptanalysis of the image encryption schemes was reported under the known and chosen plain data attacks [31][32][33][34][35][36] and the known cipher data attack [37][38][39][40][41] .…”

Section: Introductionmentioning

confidence: 99%

“…The bit-plane measures can be used to identify such images as normal images or the HE images and also to identify the unencrypted bit-planes that have some meaningful information. These can be applied in various other applications of image classification and pattern recognition [39][40] .…”

Section: Introductionmentioning

confidence: 99%

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Security Analysis of Bit plane Level Image Encryption Schemes

Ratan

Yadav

2021

Def. Sc. J.

Self Cite

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A selective bit-plane encryption scheme was proposed for securing the transmission of image data in mobile environments with a claim that it provides a high security viz. the encryption of the four most significant bit-planes is sufficient for a high image data security. This paper presents the security analysis of the said encryption scheme and reports new important results. We perform the security analysis of the bit-level encryption by considering the normal images and their histogram equalised enhanced images. We consider different bit-plane aspects to analyse the security of the image encryption, and show that the encryption of the four most significant bit-planes is not adequate. The contents of the images can be obtained even when all the bit-planes except one least significant bit-plane are encrypted in the histogram equalised images as shown in the results. The bit-plane level security analysis seems very useful for the analysis of the bit-plane level image encryption schemes.

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“…A pixel value is represented with 24 bits (8 bit for each colour band). for more detail on images, one may refer 1,2, 39,40 .…”

Section: Histogram Equalisation and Bit-plane Eencryptionmentioning

confidence: 99%

Section: Bit-plane Encryptionmentioning

confidence: 99%

Section: Identification Of He Images Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Security Analysis of Bit plane Level Image Encryption Schemes

Ratan

Yadav

2021

Def. Sc. J.

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A Block Cipher Algorithm Identification Scheme Based on Hybrid Random Forest and Logistic Regression Model

Yuan

Huang

et al. 2022

Neural Process Lett

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Cohesion measurements between variables and methods using component-based software systems

Shipra

2024

Int J Syst Assur Eng Manag

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The practice of leveraging previously created software components to progress new software is identified as component-based software engineering (CBSE). Good software engineering design is the foundation of CBSE principles. The black box approach that underpins CBSE hides the execution of components in nature, and the components communicate with one another using strictly delineated interfaces. Component platforms are shared, which lowers the price of creation. To ascertain a system's complexity, various software metrics are employed. For superiority in software intricacy, coupling would be minimal, and cohesiveness must be high. It is predetermined that coupling should be low and cohesion should be increased for refinement in software complexity. We are identifying the combination of different software systems and improving the methods for doing so with our approach. Proposed: Cohm (cohesion of methods) and Cohv (cohesion of variables) are two cohesion metrics that have been proposed. The cohesiveness metrics in this study have been analytically and empirically evaluated, and a comparison has been made between them. Additionally, an effort was made to give the outcomes of an empirical estimation based on the case study. The T-test is used to determine the consequences of the metrics, and Python is used to validate the metrics. Python or R programming and the Matlab tool are used to determine the relationship between various variables and metrics. Findings: The consequence of the current investigation is very encouraging and might be used to estimate the involvedness of the parts. The proportional analysis of the proposed metrics and various cohesion metrics reveals that the suggested metrics are more cohesive than the present metrics, increasing the likelihood that they can be reused when creating new applications.

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Identifying traffic of same keys in cryptographic communications using fuzzy decision criteria and bit-plane measures

Cited by 6 publications

References 23 publications

Security Analysis of Bit plane Level Image Encryption Schemes

Security Analysis of Bit plane Level Image Encryption Schemes

A Block Cipher Algorithm Identification Scheme Based on Hybrid Random Forest and Logistic Regression Model

Cohesion measurements between variables and methods using component-based software systems

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