Neural and Quantum Cryptography in Big Data: A Review

OzCakmak, Burak; Ozbilen, Alper; Yavanoğlu, Uraz; CIn, Kubra

doi:10.1109/bigdata47090.2019.9006238

Cited by 14 publications

(5 citation statements)

References 3 publications

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“…A brief introduction on securing BD using neural and quantum cryptography was presented by Özc ¸akmak et al in [31]. Neural cryptography combined the principle of the neural network with classical cryptography.…”

Section: E New Proposals For Securing Big Datamentioning

confidence: 99%

Securing Big Data: A Survey on Security Solutions

Elshall¹

2022

Preprint

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Section: E New Proposals For Securing Big Datamentioning

confidence: 99%

Securing Big Data: A Survey on Security Solutions

Elshall¹

2022

Preprint

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“…Artificial neural networks have been used for cryptography Zçakmak et al [39], Alani [2], Abdalrdha et al [1], Protic [26], Hadke and Kale [12]. A lot of work has been done on this.…”

Section: Related Workmentioning

confidence: 99%

Mutual learning-based efficient synchronization of neural networks to exchange the neural key

Sarkar

2021

Complex Intell. Syst.

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Synchronization of two neural networks through mutual learning is used to exchange the key over a public channel. In the absence of a weight vector from another party, the key challenge with neural synchronization is how to assess the coordination of two communication parties. There is an issue of delay in the current techniques in the synchronization assessment that has an impact on the security and privacy of the neural synchronization. In this paper, to assess the complete coordination of a cluster of neural networks more efficiently and timely, an important strategy for assessing coordination is presented. To approximately determine the degree of synchronization, the frequency of the two networks having the same output in prior iterations is used. The hash is used to determine if both the networks are completely synchronized exactly when a certain threshold is crossed. The improved technique makes absolute coordination between two communication parties using the weight vectors’ has value. In contrast, with existing approaches, two communicating parties who follow the proposed approach will detect complete synchronization sooner. This reduces the effective geometric likelihood. The proposed method, therefore, increases the safety of the protocol for neural key exchange. This proposed technique has been passed through different parametric tests. Simulations of the process show effectiveness in terms of cited results in the paper.

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“…The embedding vector of the entire binary function was then generated by feeding the simple blocks' numerical vectors to the custom neural semantics model. Neural networks have been commonly used for cryptography [5], [6], [7], [8], [9]. Santhanalakshmi et al [10] introduced the genetic algorithm to synchronize neural networks to find optimum weights as initial weights.…”

Section: Related Workmentioning

confidence: 99%

Nature-Inspired Gravitational Search-Guided Artificial Neural Key Exchange for IoT Security Enhancement

et al. 2021

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The advantages of IoT are incontrovertible; however, significant risks and uncertainties over the protection of best practices and their related costs prevent many companies from adopting the technology. Also, users are aware of the consequences of breaches of IoT protection. Recent research indicates that 90% of consumers lack confidence in the safety of IoT devices. IoT defense has been described as one of the weakest areas. In this respect, significant efforts have been made, often through traditional cryptographic methods, to overcome security and privacy issues in IoT systems. The list of security issues (confidentiality, integrity, authentication, and availability) must be addressed to secure IoT devices from attack. IoT nodes' specific features do not adequately protect the whole security spectrum of IoT networks through existing solutions. This paper aims to provide security to the IoT devices using nature-inspired Gravitational Search-guided artificial neural key. In this paper, artificial neural synchronization is used to create a neural key exchange protocol between two IoT devices over a public channel for cryptographic purposes. This proposed technique has many benefits, such as 1) it provides an optimized design of the neural network that facilitates the development and establishment of a neural key between the two approved IoT devices. 2) It offers the sharing of the private neural key over the public channel via artificial neural synchronization 3)Three hidden layers of the neural network contribute to the deep internal design. So, it's going to be hard for the intruder to infer the internal layout. 4) The increase in the weight spectrum of the neural network raises the complexity of an effective attack exponentially, but the effort to create a neural key decreases over polynomial time. Various parametric experiments have been conducted out on the proposed methodology. The simulations of the procedure demonstrate efficacy in terms of the findings cited in the paper.

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Neural and Quantum Cryptography in Big Data: A Review

Cited by 14 publications

References 3 publications

Securing Big Data: A Survey on Security Solutions

Securing Big Data: A Survey on Security Solutions

Mutual learning-based efficient synchronization of neural networks to exchange the neural key

Nature-Inspired Gravitational Search-Guided Artificial Neural Key Exchange for IoT Security Enhancement

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