Multilayer neural network synchronized secured session key based encryption in wireless communication

Sarkar, Arindam; Dey, Joydeep; Bhowmik, Anirban

doi:10.11591/ijeecs.v14.i1.pp169-177

Cited by 21 publications

(13 citation statements)

References 8 publications

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“…The BLOSUM50 complementary rule as given in Table 3 has been obtained. The proposed binary sequence would be then passed to isosceles encryption technique [ 38 ]. The cipher matrix will be generated depending on the orientation of the key bits as given in Table 4 .…”

Section: Results Sectionsmentioning

confidence: 99%

See 1 more Smart Citation

Wireless COVID-19 Telehealth: Leukocytes Encryption Guided by Amino Acid Matrix

Dey¹,

Mukherjee²

2021

Wireless Pers Commun

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Section: Results Sectionsmentioning

confidence: 99%

“…In the Telecare E-Health system, patients’ medical data has to be kept secured [ 36 , 38 , 39 ]. This is the biggest challenge for the researchers.…”

Section: Statistical Key Analysismentioning

confidence: 99%

Wireless COVID-19 Telehealth: Leukocytes Encryption Guided by Amino Acid Matrix

Dey¹,

Mukherjee²

2021

Wireless Pers Commun

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“…Kinzel and Kanter [26] and Ruttor et al [42] described that a chance of an attack is declining if the network's weight range get increases and the assailant's calculation cost increases, as its effort increases exponentially, as users' effort becomes polynomial. Sarkar and Mandal [44], Sarkar et al [48], Sarkar et al [46], Sarkar and Mandal [45], Sarkar et al [47] proposed schemes which enhanced the security of the protocol by enhancing the synaptic depths of TPM and henceforth counteracting the attacks of the brute power of the attacker. It is found that the amount of security provided by the TPM synchronization also can be improved by introducing a large set of neurons and entries of each neuron in the hidden layers.…”

Section: Related Workmentioning

confidence: 99%

Generative adversarial network guided mutual learning based synchronization of cluster of neural networks

Sarkar

2021

Complex Intell. Syst.

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Neural synchronization is a technique for establishing the cryptographic key exchange protocol over a public channel. Two neural networks receive common inputs and exchange their outputs. In some steps, it leads to full synchronization by setting the discrete weights according to the specific rule of learning. This synchronized weight is used as a common secret session key. But there are seldom research is done to investigate the synchronization of a cluster of neural networks. In this paper, a Generative Adversarial Network (GAN)-based synchronization of a cluster of neural networks with three hidden layers is proposed for the development of the public-key exchange protocol. This paper highlights a variety of interesting improvements to traditional GAN architecture. Here GAN is used for Pseudo-Random Number Generators (PRNG) for neural synchronization. Each neural network is considered as a node of a binary tree framework. When both i-th and j-th nodes of the binary tree are synchronized then one of these two nodes is elected as a leader. Now, this leader node will synchronize with the leader of the other branch. After completion of this process synchronized weight becomes the session key for the whole cluster. This proposed technique has several advantages like (1) There is no need to synchronize one neural network to every other in the cluster instead of that entire cluster can be able to share the same secret key by synchronizing between the elected leader nodes with only logarithmic synchronization steps. (2) This proposed technology provides GAN-based PRNG which is very sensitive to the initial seed value. (3) Three hidden layers leads to the complex internal architecture of the Tree Parity Machine (TPM). So, it will be difficult for the attacker to guess the internal architecture. (4) An increase in the weight range of the neural network increases the complexity of a successful attack exponentially but the effort to build the neural key decreases over the polynomial time. (5) The proposed technique also offers synchronization and authentication steps in parallel. It is difficult for the attacker to distinguish between synchronization and authentication steps. 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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“…Gupta and Deshmukh [20] applied the protocol proposed in [8] to encrypt and decrypt the image secret sharing. Sarkar [21] applied the session key generated by the synchronization of multilayer perceptron to wireless communications. Shishniashvili et al [22] proposed a technique to use parts of the weight vector instead of the entire weight vector as the session key.…”

Section: Related Workmentioning

confidence: 99%

An Improved Method to Evaluate the Synchronization in Neural Key Exchange Protocol

Han¹,

Liu²,

Li³

et al. 2020

Security and Communication Networks

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The synchronization between two neural networks by mutual learning can be used to design the neural key exchange protocol. The critical issue is how to evaluate the synchronization without a weight vector. All existing methods have a delay in evaluating the synchronization, which affects the security of the neural key exchange. To evaluate the full synchronization of neural networks more timely and accurately, an improved method for evaluating the synchronization is proposed. First, the frequency that the two networks have the same output in previous steps is used for assessing the degree of them roughly. Second, the hash function is utilized to judge whether the two networks have achieved full synchronization precisely when the degree exceeds a given threshold. The improved method can find the full synchronization between two networks with no information other than the hash value of the weight vector. Compared with other methods, the full synchronization can be detected earlier by two communication partners which adopt the method proposed in this paper. As a result, the successful probability of geometric is reduced. Therefore, the proposed method can enhance the security of the neural exchange protocol.

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Multilayer neural network synchronized secured session key based encryption in wireless communication

Cited by 21 publications

References 8 publications

Wireless COVID-19 Telehealth: Leukocytes Encryption Guided by Amino Acid Matrix

Wireless COVID-19 Telehealth: Leukocytes Encryption Guided by Amino Acid Matrix

Generative adversarial network guided mutual learning based synchronization of cluster of neural networks

An Improved Method to Evaluate the Synchronization in Neural Key Exchange Protocol

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