Construction of multivalued cryptographic boolean function using recurrent neural network and its application in image encryption scheme

Abughazalah, Nabilah; Latif, Asim; Hafiz, Muhammad Waseem; Khan, Majid; Alanazi, Ammar S.; Hussain, Iqtadar

doi:10.1007/s10462-022-10295-1

Cited by 10 publications

(4 citation statements)

References 126 publications

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“…A multivalued cryptographic Boolean function employing a recurrent neural network was recently developed 91 . The network generates balanced confusion components with low linear and differential probability and a nonlinearity of 112.…”

Section: Discussionmentioning

confidence: 99%

Design of highly nonlinear confusion component based on entangled points of quantum spin states

Waseem

Hwang

2023

Sci Rep

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Cryptosystems are commonly deployed to secure data transmission over an insecure line of communication. To provide confusion in the data over insecure networks, substitution boxes are the solitary components for delivering a nonlinear mapping between inputs and outputs. A confusion component of a block cipher with high nonlinearity and low differential and linear approximation probabilities is considered secure against cryptanalysis. This study aims to design a highly nonlinear substitution-permutation network using the blotch symmetry of quantum spin states on the Galois field GF (28). To observe the efficiency of the proposed methodology, some common and advanced measures were evaluated for performance, randomness, and cryptanalytics. The outcomes of these analyses validate that the generated nonlinear confusion components are effective for block ciphers and attain better cryptographic strength with a high signal-to-noise ratio in comparison to state-of-the-art techniques.

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Section: Discussionmentioning

confidence: 99%

Design of highly nonlinear confusion component based on entangled points of quantum spin states

Waseem

Hwang

2023

Sci Rep

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“…In [214], Zhang et al proposed a novel Deep Reinforcement Learning approach for Image Hashing (DR-LIH), presenting hashing learning as a sequential decisionmaking process to enhance retrieval accuracy. This pioneering method uses recurrent neural networks (RNNs) [215] as agents within a hashing network, allowing sequential actions for image projection into binary codes and considering errors from previous functions. Furthermore, a sequential learning strategy in DRLIH captures decision-making that makes the combined state representation of the internal and image features of RNN.…”

Section: A Deep Hashingmentioning

confidence: 99%

Advances and Vulnerabilities in Modern Cryptographic Techniques: A Comprehensive Survey on Cybersecurity in the Domain of Machine/Deep Learning and Quantum Techniques

Mehmood,

Shafique,

Alawida

et al. 2024

IEEE Access

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In the contemporary landscape, where a huge amount of data plays a vital role, the importance of strong and robust cybersecurity measures has become increasingly paramount. This research proposes a review and extensively explores cybersecurity techniques within the domain of machine/deep learning and quantum techniques, with a particular focus on cryptographic methods and methodologies applied to image encryption. The proposed survey covers a range of cybersecurity techniques, including quantum random number generation, secure transmission of quantum images, watermarking through quantum methods, and quantum steganography. Moreover, it explores the domain of image encryption, which integrates adversarial neural networks, deep learning and machine learning, transformation techniques, and chaotic neural networks that can be used to secure digital data from cyber attacks. Our focus extends beyond highlighting advances in investigating vulnerabilities in existing cryptographic techniques. By identifying the challenges and weaknesses, the potential solutions are also presented, establishing a foundation for future recommendations. These future suggestions address and overcome the vulnerabilities observed in existing cybersecurity techniques. The aim of the extensive survey and analysis of existing cryptographic techniques is to provide a deep understanding of innovative and diverse approaches within the cybersecurity domain. Simultaneously, it aims to create a roadmap for the future to counter potential cyber threats and challenges.

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“…Boolean functions play a fundamental role in constructing S-boxes, which are critical components of various cryptographic algorithms. S-boxes, or substitution boxes, are used to perform substitutions on input data during the encryption process, adding a layer of complexity that enhances the security of the algorithm [1,4,25].…”

Section: Boolean Functionsmentioning

confidence: 99%

Construction of Substitution Boxes Using Finite Fields

Umrani,

Pali,

Dahri

2023

VFAST trans. math.

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Substitution boxes (S-boxes) play a crucial role in modern cryptographic algorithms, providingnon-linear transformations that enhance the security of data encryption. S-boxes are essentialcomponents of symmetric key ciphers, such as Advanced Encryption Standard (AES) and DataEncryption Standard (DES). Non-linearity is a fundamental property that resists linear attacksand enhances the cryptographic strength of S-boxes. Constructing secure and efficient S-boxeswith high non-linearity is a critical challenge in cryptography. The problem of this research isto develop a systematic approach for constructingsubstitution boxes (S-boxes) using finite fields,with a particular focus on achieving optimal non-linearity to enhance the security of cryptographicalgorithms.

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Construction of multivalued cryptographic boolean function using recurrent neural network and its application in image encryption scheme

Cited by 10 publications

References 126 publications

Design of highly nonlinear confusion component based on entangled points of quantum spin states

Design of highly nonlinear confusion component based on entangled points of quantum spin states

Advances and Vulnerabilities in Modern Cryptographic Techniques: A Comprehensive Survey on Cybersecurity in the Domain of Machine/Deep Learning and Quantum Techniques

Construction of Substitution Boxes Using Finite Fields

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