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\title{Enhance the Performance of Chaotic Generator in the Filed of Cryptography: A Secret Key Generation Approach}
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\begin{document}

             \author[1]{Sona  Mishra}

             \author[2]{Amit  Mishra}

             \affil[1]{  VITS Jabalpur ,University of R.G.P.V. Bhopal M.P.}

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\date{\small \em Received: 15 December 2014 Accepted: 2 January 2015 Published: 15 January 2015}

\maketitle


\begin{abstract}
        


The main focus of this research paper is to propose and improvement of the data security using encryption and decryption method in ANN based chaotic generator of original value. The Binary value sequence of ASCII CODE is converted with two initial parameter, and converted value is again decrypted with same initial parameter. In which consists of Binary value of ASCII Code, chaotic neural network algorithm was used for encryption and decryption and it generates the chaotic sequence of random value for each A to Z letter. The generated random value is the encrypted binary ASCII values of A to Z sequence of original ASCII Code binary value, with same initial parameter. For simulation MATLAB software is used. This paper also includes improved experimental results and complete demonstration that ANN Based Chaotic Generator is successfully perform the cryptography.

\end{abstract}


\keywords{ann based chaotic generator, chaotic neural network, cryptography.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
ryptography is the exchange of information among the users without leakage of information to others. Many public key cryptography are available which are based on number theory but it has the drawback of requirement of large computational power, complexity and time consumption during generation of key \hyperref[b0]{[1]}.\par
Cryptosystems are commonly used for protecting the integrity, confidentiality, and authenticity of information resources. In addition to meeting standard specifications relating to encryption and decryption, such systems must meet increasingly stringent specifications concerning information security. This is mostly due to the steady demand to protect data and resources from disclosure, to guarantee the authenticity of data, and to protect systems from web based attacks. For these reasons, the development and evaluation of cryptographic algorithms is a challenging task \hyperref[b1]{[2]}.\par
This paper is study and performance of ANN Based Chaotic Generator in the filed of Cryptography. The rest of the paper is organized as follows: section 2 discusses background and related work in the field of chaotic neural network based cryptography, section 3\par
Author ? ? ? : VITS Jabalpur, University of R.G.P.V. Bhopal M.P. e-mails: sona.mishra2909@gmail.com, amit\textunderscore 2440@yahoo.co.in discusses implementation section 4 discusses experimental report and test result and finally section 5 discusses conclusion. 
\section[{II.}]{II.} 
\section[{Background and Related Work}]{Background and Related Work}\par
Ilker DALKIRAN, Kenan DANIS¸MAN introduced a research paper on Artificial neural network based chaotic generator for cryptology. In this paper, to overcome disadvantages of chaotic systems, the dynamics of Chua's circuit namely x, y and z were modeled using Arti ficial Neural Network (ANN). ANNs have some distinctive capabilities like learning from experiences, generalizing from a few data and nonlinear relationship between inputs and outputs. The proposed ANN was trained in diffrent structures using different learning algorithms. To train the ANN, 24 different sets including the initial conditions of Chua's circuit were used and each set consisted of about 1800 input-output data. The experimental results showed that a feedforward Multi Layer Perceptron (MLP), trained with Bayesian Regulation back propagation algorithm, was found as the suitable network structure. As a case study, a message was first encrypted and then decrypted by the chaotic dynamics obtained from the proposed ANN and a comparison was made between the proposed ANN and the numerical solution of Chua's circuit about encrypted and decrypted messages  {\ref [}   
\section[{b) Proposed Work}]{b) Proposed Work}\par
In Cryptography, secret key generation scheme was proposed by ANN based Chaotic Generator. ANN based chaotic Generator system used chaotic neural network scheme for encryption and decryption  {\ref [7]}. In this paper ANN based chaotic generator is proposed for data encryption and decryption, it produces the outputs according to initial conditions and control parameter .We improve the level of performance of chaos based cryptography [10] using binary value of ASCII Code of A to Z letter instead of decimal value. A plain-text was encrypted and then obtained cipher text was decrypted by using the chaotic dynamics (control parameter and initial point), initial condition and control parameter act as a secret key in the field of cryptography. It is accepted that the initial conditions which were used in the training phase of the ANN model and the system parameters are known by both the transmitter and the receiver.\par
We adopted ANN based chaotic generator approach from et.al.  {\ref [3]} and increase the level of security from et. al.\par
[10] and demonstrate by experimental result. 
\section[{III.}]{III.} 
\section[{Implementation}]{Implementation}\par
A network is called chaotic neural network if its weights and biases are determined by chaotic sequence. In this section we use a algorithm for performing encryption and decryption using chaotic neural network. ANN based chaotic generator Using CNN scheme for encryption and decryption.\par
Step 1. The chaotic Logistic map. 
\section[{Set the value of M.}]{Set the value of M.}\par
Step 2. The secret key is the control parameter ? and the initial point x (0) of the Logistic map, which are all Lbit binary decimals. Determine parameter ? and initial point x (0).\par
Step 3. The initialization procedure:\par
Generate the chaotic sequence x(1), x(2), x(3)? .\par
. Step 4. The encryption procedure:\par
Depending upon the chaotic sequence a weight matrix and a bias matrix is obtained and the net input is obtained. Then a hard limiter is applied as a transfer function in order to obtain the digital encrypted data. For decryption the same network is used and the same initial value is used to generate the chaotic sequence and for decrypting the data successfully. Step 5. The decryption procedure The decryption procedure is the same as the above one except that the input signal to the decryption Chaotic neural network should be g'(n) and its output signal should be g"(n).  V. 
\section[{b) ANN based chaotic Generator}]{b) ANN based chaotic Generator} 
\section[{Conclusion}]{Conclusion}\par
It is clear that the binary value sequence of ASCII CODE is encrypted and decrypted correctly by knowing the exact values of x (0) and µ otherwise we get the wrong value sequences .And also clear that the binanry value is more strong enough as compre to decimal values. In this paper we successfully perform encryption and decryption with the help of Chaotic neural network and improve the level of security with the help of using binanry value of ASCII Code instead of decimal values . Network was trained with the help of back propagation algorithm in neural network. Above experiment clear that the Binary value of ASCII CODE is encrypted and its decrypted with same value of parameter , encrypted value is decrypt only correctly by knowing the exact values of x (0) and µ otherwise we get the wrong generated value sequences . ANN based Chaotic generator provide high range of security in the field of cryptography.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}2 GlobalFor}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{:}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.8234374999999999\textwidth}P{0.0028977272727272723\textwidth}P{0.015454545454545453\textwidth}P{0.0053125\textwidth}P{0.0028977272727272723\textwidth}}
NETWORKS IN CRYPTOSYSTEMS. This paper presents\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
a review of the literature on the use of artificial neutral\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
networks in cryptography. Different neural network\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
based approaches have been categorized based on\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
their applications to different components of\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
cryptosystems such as secret key protocols, visual\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
cryptography, design of random generators, digital\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
watermarking, and steganalysis[2].\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
KARAM M. Z. OTHMAN , MOHAMMED H. AL\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
JAMMAS introduced IMPLEMENTATION OF NEURAL -\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CRYPTOGRAPHIC SYSTEM USING FPGA. In this work,\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
a Pseudo Random Number Generator (PRNG) based on\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
artificial Neural Networks (ANN) has been designed.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
This PRNG has been used to design stream cipher\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
system with high statistical randomness properties of its\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
key sequence using ANN. Software simulation has been\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
build using MATLAB to firstly, ensure passing four well-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
known statistical tests that guaranteed randomness\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
characteristics [6].\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
An Empirical Investigation of Using ANN Based\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
N-\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Keywords: ann based chaotic generator, chaotic neural\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
network, cryptography.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep 3].\tabcellsep \\
\multicolumn{5}{l}{Jason L. Wright , Milos Manic Proposed a}\\
\multicolumn{2}{l}{research paper on}\tabcellsep \multicolumn{3}{l}{Neural Network Approach to}\\
\multicolumn{5}{l}{Locating Cryptography in Object Code. In this paper,}\\
\multicolumn{5}{l}{artificial neural networks are used to classify functional}\\
\multicolumn{5}{l}{blocks from a disassembled program as being either}\\
\multicolumn{5}{l}{cryptography related or not. The resulting system,}\\
\multicolumn{5}{l}{referred to as NNLC (Neural Net for Locating}\\
\multicolumn{5}{l}{Cryptography) is presented and results of applying this}\\
\multicolumn{4}{l}{system to various libraries are described.[4].}\tabcellsep \\
Eva\tabcellsep Volna,\tabcellsep Martin\tabcellsep Kotyrba,\tabcellsep Vaclav\\
\multicolumn{5}{l}{Kocian,Michal Janosek developed a CRYPTOGRAPHY}\\
\multicolumn{5}{l}{BASED ON NEURAL NETWORK. This paper deals with}\\
\multicolumn{5}{l}{using neural network in cryptography, e.g. designing}\\
\multicolumn{5}{l}{such neural network that would be practically used in}\\
\multicolumn{5}{l}{the area of cryptography. This paper also includes an}\\
\multicolumn{3}{l}{experimental demonstration [5].}\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: T. SCHMIDT, H. RAHNAMA developed A REVIEW OF APPLICATIONS OF ARTIFICIAL NEURAL Abstract]} 
\caption{\label{tab_0}}\end{figure}
 			\footnote{© 2015 Global Journals Inc. (US) 1} 			\footnote{© 2015 Global Journals Inc. (US)} 		 		\backmatter   			  			  			  				\begin{bibitemlist}{1}
\bibitem[Shweta et al. (2012)]{b0}\label{b0} 	 		‘Nawgaje-a triple-key chaotic neural network for cryptography in image processing’.  		 			B Shweta 		,  		 			Suryawanshi 		,  		 			D Devesh 		.  	 	 		\textit{International Journal of Engineering Sciences \& Emerging Technologies}  		 2231 -6604.  		April 2012. 2  (1)  p. .  	 
\bibitem[Schmidt and Dept]{b1}\label{b1} 	 		\textit{of computer science, ryerson university, canada -a review of applications of artificial neural networks in cryptosystems},  		 			T Schmidt 		,  		 			Dept 		.  		 	 
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