Computational Intelligence Based Neural Session Key Generation on E-Health System for Ischemic Heart Disease Information Sharing

Sarkar, Arindam; Dey, Joydeep; Bhowmik, Anirban; Mandal, Jyotsna Kumar; Karforma, Sunil

doi:10.1007/978-981-13-1540-4_3

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…Kinzel and Kanter [15] and Ruttor et al [16] identified that if the weight of the network increases, the attack probability decreases and the assailant's computational cost grows exponentially as the user effort becomes polynomial. Sarkar and Mandal [17] and Sarkar et al [18]- [21] proposed schemes to enhance the security of the protocol by improving the synaptic depths of TPM and henceforth counteracting the attacks of the brute strength of the attacker. It is found that the amount of security provided by TPM synchronization can also be improved by inserting a large collection of neurons and entries of each neuron into the hidden layers.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

et al. 2021

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In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.The associate editor coordinating the review of this manuscript and approving it for publication was Jiafeng Xie.secret while he may be able to follow the structure of the algorithm. Chen et al. [2]; Liu et al. [3]; Chen et al. [4]; Wang et al. [5], [6]; Xiao et al. [7]; Zhang and Cao [8]; Wang et al. [9]; Dong et al. [10] described that the neural network synchronization approach offers the chance to solve enormous exchange problems. Rosen-Zvi et al. [11]; Lakshmanan et al. [12] ; Ni and Paul [13] recently showed that neural cryptography has the capability of achieving key exchange through neural synchronization of Artificial Neural Networks (ANN). This proposed technique uses an ANN called the Double Layer Tree Parity Machine (DLTPM). By generating common inputs and sharing the outputs of such networks,

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Section: Related Workmentioning

confidence: 99%

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

et al. 2021

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“…Efficient management of key space makes the brute force attack [45] infeasible. In this attack, attacker tries to translate the cipher text into plain text using every possible key.…”

Section: Brute Force Attacksmentioning

confidence: 99%

Cryptographic Engineering on COVID-19 Telemedicine: An Intelligent Transmission Through Recurrent Relation Based Session Key

DEY¹,

BHOWMIK²,

Sarkar

et al. 2021

Preprint

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Constraints imposed due to the cameo of the novel coronavirus has abruptly changed the operative mode of medical sciences. Most of the hospitals have migrated towards the telemedicine mode of services of the non-invasive and non-emergency patients during the COVID-19 time. The advent of telemedicine services has remotely rendered health services to different types of patients from their quarantines. Here, the patients’ medical data has to be transmitted to different physicians / doctors. Such data are to be secured with a view to restore its privacy clause. Cardio vascular diseases (CVDs) are a kind of cardiac disease related to blockage of arteries and veins. This paper presents an intelligent and secured transmission of cardiac reports of the patients through recurrence relation based session key. Such reports were made through the following confusion matrix operations. The beauty of this technique is that confusion matrices are transferred to specified number of cardiologists with further secret shares encapsulation. The case of robustness checking, transparency and cryptographic engineering has been tested under different inputs. Different types of result and its analysis proves the efficiency of the proposed technique. It will provide more security in medical data transmission, especially in the needy hours of COVID-19 pandemic.

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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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Computational Intelligence Based Neural Session Key Generation on E-Health System for Ischemic Heart Disease Information Sharing

Cited by 12 publications

References 8 publications

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

Cryptographic Engineering on COVID-19 Telemedicine: An Intelligent Transmission Through Recurrent Relation Based Session Key

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

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