Secured Wireless Communication using Fuzzy Logic based High Speed Public-Key Cryptography (FLHSPKC)

Sarkar, Arindam; Mandal, Jyotsna Kumar

doi:10.14569/ijacsa.2012.031023

Cited by 10 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The measurement of the distance between weights using the rule of Euclidean is not even technically applicable. Sarkar and Mandal [34], Sarkar [33], Sarkar and Mandal [35], Mandal and Sarkar [19], Sarkar et al [36] proposed schemes that 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%

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

Sarkar

2021

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Sarkar

2021

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This approach took a long time to complete the synchronization process. Sarkar et al [14], [15], [16], [17], [18] suggested a framework that increased protocol security by improving the Tree Parity Machine's weight range (TPM). It was discovered that increasing the number of neurons in the secret layers increases the level of TPM synchronization security.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The fuzzification is the transformation of numerical data from the input to linguistic terms. The knowledge base provides necessary information for all the components of the fuzzy controller [9][10][11]. The fuzzy inference engine or the logical decision-making is the core (brain) of the controller.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

The Application of Fuzzy Control in Water Tank Level Using Arduino

Chabni¹,

Taleb²,

Benbouali³

et al. 2016

ijacsa

View full text Add to dashboard Cite

Abstract-Fuzzy logic control has been successfully utilized in various industrial applications; it is generally used in complex control systems, such as chemical process control. Today, most of the fuzzy logic controls are still implemented on expensive highperformance processors. This paper analyzes the effectiveness of a fuzzy logic control using a low-cost controller applied to a water level control system. The paper also gives a low-cost hardware solution and practical procedure for system identification and control. First, the mathematical model of the process was obtained with the help of Matlab. Then two methods were used to control the system, PI (Proportional, Integral) and fuzzy control. Simulation and experimental results are presented.

show abstract

Secured Wireless Communication using Fuzzy Logic based High Speed Public-Key Cryptography (FLHSPKC)

Cited by 10 publications

References 16 publications

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

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

The Application of Fuzzy Control in Water Tank Level Using Arduino

Contact Info

Product

Resources

About