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

2019 ◽  
Vol 8 (1) ◽  
pp. 44 ◽  
Author(s):  
Arindam Sarkar
Keyword(s):  
Neural Network ◽  
Wireless Communication ◽  
Multilayer Perceptron ◽  
Secret Key ◽  
Session Key ◽  
Chi Square ◽  
Plain Text ◽  
Chi Square Test ◽  
Hidden Layer ◽  
Final Block

In this paper, multilayer neural network synchronized session key based encryption has been proposed for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights or hidden units of this selected hidden layer help to form a secret session key. The plain text is encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the final block of plain  text is less than the size of the key then this block is kept unaltered.  Receiver will use identical multilayer perceptron generated session key for performing deciphering process for getting the plain text. Parametric tests have been  done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed technique.

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

2019 ◽  
Vol 14 (1) ◽  
pp. 169 ◽  
Author(s):  
Arindam Sarkar ◽  
Joydeep Dey ◽  
Anirban Bhowmik
Keyword(s):  
Neural Network ◽  
Wireless Communication ◽  
Frequency Analysis ◽  
Multilayer Perceptron ◽  
Secret Key ◽  
Session Key ◽  
Novel Approach ◽  
Layered Networks ◽  
Hidden Layer ◽  
Energy Computation

<p>Energy computation concept of multilayer neural network synchronized on derived transmission key based encryption system has been proposed for wireless transactions. Multilayer perceptron transmitting machines accepted same input array, which in turn generate a resultant bit and the networks were trained accordingly to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights of hidden units of this selected hidden layer help to form a secret session key. A novel approach to generate a transmission key has been explained in this proposed methodology. The last thirty two bits of the session key were taken into consideration to construct the transmission key. Inverse operations were carried out by the destination perceptron to decipher the data. Floating frequency analysis of the proposed encrypted stream of bits has yielded better degree of security results. Energy computation of the processed nodes inside multi layered networks can be done using this proposed frame of work.</p>

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

scholarly journals Notice of Retraction Coupled Tree Parity Machines: Synchronized Secured Session Key based Encryption in Online Transaction

2019 ◽  
Vol 4 (1) ◽  
pp. 27-36
Author(s):  
Arindam Sarkar ◽  
Joydeep Dey ◽  
Sunil Karforma ◽  
Anirban Bhowmik
Keyword(s):  
Data Communication ◽  
Weight Vector ◽  
Expert Committee ◽  
Brute Force ◽  
Secret Key ◽  
Session Key ◽  
Online Data ◽  
Plain Text ◽  
Degree Of Coupling ◽  
Tree Parity Machine

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ij.aptikom@gmail.com.----------------------------------------------------------------------- In this paper, tree parity synchronized session key validation followed by encryption has been proposed for online data communication. Tree Parity Machine transmitting systems at both ends accepted an identical input vector, generated an output bit, validated the weight vector and the networks were trained accordingly based on the output bit which was used to form a protected variable length secret key. Existence of a better degree of coupling between the two topological same tree parity machines has been reflected in this paper. Instead of sharing the entire weight vector, the proposed technique guided the partial transmission and validation of session key. A string of sub key has been derived from the synchronized session key for initial ciphering matrix. The plain text was encrypted through single columnar transposition ciphering at first round of encryption followed by successive cascaded XORing of TPM generated session key. If size of the final block of plain text was less than the size of the key then this block was treated unaltered.  Recipient used identical generated session key for performing deciphering process for getting the plain text. Brute force attacks analysis has been implemented which determines a higher amount of time to decrypt by the intruders. Such long computational operations were not feasible by any of randomly selected fast networks at the intruders’ terminals.

New Key-Dependent S-Box Generation Algorithm on AES

2021 ◽  
Vol 13 ◽  
Author(s):  
Proteeti Das ◽  
Najmul Hoque Munshi ◽  
Subhasis Maitra
Keyword(s):  
Data Encryption ◽  
Secret Key ◽  
Chi Square ◽  
Avalanche Effect ◽  
Generation Algorithm ◽  
Proposed Model ◽  
Chi Square Test ◽  
Overall Performance ◽  
Cryptographic Techniques ◽  
Low Charge

Aims: Cryptography means 'hidden secrets'. The primary purpose of cryptography is to protect network and data over a wireless communication channel. The cryptographic approach secures the data of a network from any internal or external attacks. Background: There are several kinds of cryptographic techniques that are Data Encryption Standard (DES), RSA (Rivest- Shamir- Adleman), Advanced Encryption Standard (AES), Blowfish, Twofish etc. Out of these algorithms, AES shows wide acceptance for its superiority in providing confidentiality to secret information. Another cause for extensive acceptance is, AES is simple, convenient to implement, low charge and higher security. Several changes have been proposed to modify in recent times by cryptographers and researchers all around the world. Objective: This research paper offers a new key-dependent s-box generation algorithm for AES. Methods: A list of irreducible polynomials of degree 8 is used to generate the s-box depending on the secret key to provide more invulnerable ciphertext in comparison to standard AES. This design of this proposed model is easy and convenient to implement than different dynamic s-box technology algorithm. Results : The metrics chosen for overall performance evaluation are Frequency Distribution, Chi-square Test, Avalanche Effect, and Strict Avalanche Criterion. Conclusion: The proposed algorithm satisfies the desired property of these metrics and provides better security in contrast to standard AES.

An INS Fault Diagnosis Method Based on State Chi-Square Test and Optimized GA-BP Neural Network

2021 ◽  
pp. 871-882
Author(s):  
Yinglin Ji ◽  
Falin Wu ◽  
Yachong Zhang ◽  
Yushuang Liu ◽  
Zhidong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Chi Square ◽  
Chi Square Test ◽  
Diagnosis Method

scholarly journals Using Neural Network with Speaker Applications

2010 ◽  
Vol 7 (2) ◽  
pp. 1076-1081
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Network ◽  
Automatic Speech Recognition ◽  
Multilayer Perceptron ◽  
Speaker Identification ◽  
Test Results ◽  
Identification Performance ◽  
Open Set ◽  
Present Test ◽  
Hidden Layer ◽  
Timit Database

In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty. We also present a method for selecting the speakers used for MLP training which further improves identification performance.

scholarly journals Emotion Recognition of Chinese Paintings at the Thirteenth National Exhibition of Fines Arts in China Based on Advanced Affective Computing

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Li ◽  
Dongliang Chen ◽  
Ning Yu ◽  
Ziping Zhao ◽  
Zhihan Lv
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Chinese Painting ◽  
Training Set ◽  
Chi Square ◽  
Test Set ◽  
Chi Square Test ◽  
Multimodal Emotion Recognition ◽  
Traditional Chinese Painting

Today, with the rapid development of economic level, people’s esthetic requirements are also rising, they have a deeper emotional understanding of art, and the voice of their traditional art and culture is becoming higher. The study expects to explore the performance of advanced affective computing in the recognition and analysis of emotional features of Chinese paintings at the 13th National Exhibition of Fines Arts. Aiming at the problem of “semantic gap” in the emotion recognition task of images such as traditional Chinese painting, the study selects the AlexNet algorithm based on convolutional neural network (CNN), and further improves the AlexNet algorithm. Meanwhile, the study adds chi square test to solve the problems of data redundancy and noise in various modes such as Chinese painting. Moreover, the study designs a multimodal emotion recognition model of Chinese painting based on improved AlexNet neural network and chi square test. Finally, the performance of the model is verified by simulation with Chinese painting in the 13th National Exhibition of Fines Arts as the data source. The proposed algorithm is compared with Long Short-Term Memory (LSTM), CNN, Recurrent Neural Network (RNN), AlexNet, and Deep Neural Network (DNN) algorithms from the training set and test set, respectively, The emotion recognition accuracy of the proposed algorithm reaches 92.23 and 97.11% in the training set and test set, respectively, the training time is stable at about 54.97 s, and the test time is stable at about 23.74 s. In addition, the analysis of the acceleration efficiency of each algorithm shows that the improved AlexNet algorithm is suitable for processing a large amount of brain image data, and the acceleration ratio is also higher than other algorithms. And the efficiency in the test set scenario is slightly better than that in the training set scenario. On the premise of ensuring the error, the multimodal emotion recognition model of Chinese painting can achieve high accuracy and obvious acceleration effect. More importantly, the emotion recognition and analysis effect of traditional Chinese painting is the best, which can provide an experimental basis for the digital understanding and management of emotion of quintessence.

Sign in / Sign up

Export Citation Format

Share Document