ARABIC Cryptography Technique Using Neural Network and Genetic Algorithm

International Research Journal of Computer Science (IRJCS) ISSN: 2393-9842 www.irjcs.com )6201 May( 3lume Vo, 50Issue

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ARABIC Cryptography Technique Using Neural Network and Genetic Algorithm

Shaza D. Rihan Dr.Saif Eldin F.Osma

Omdurman Islamic University Emirates College for Science and Technology Research and Islamic world studies center Computer science Department Khartoum, Sudan Khartoum, Sudan

Abstract- Cryptography is the science of Encrypting/Decrypting information. The goals of cryptography is to keep message confidentiality, message integrity and sender authentication. The techniques used to encrypt information in Arabic language are few and old. The neural net application represents a way of good cryptography technique in English language. This proposed study introduces cryptography technique for Arabic language using neural network. An experiment will be conducted to evaluate the proposed technique.

Key words: Encryption, Decryption, Neural network, a genetic algorithm

I. INTRODUCTION:

As stated in [16, 17] information security is the collection of technologies, standards, policies and management practices that are applied to information to keep it secure. There are many methods to secure information the most important is the cryptographic methods which enhance the security of digital contents and have gained high significance in the current era [22]. Cryptography has a long and fascinating history since its initial and limited use by the Egyptians some 4000 years ago [2]. Cryptography algorithms become much more important in data transmission through unsecured channel [18]. The cryptography is done by encryption which is the transformation of data into some unreadable form and decryption which is the reverse of encryption; it is the transformation of encrypted data back into some intelligible form [13]. Arabic language is the one of widely spoken language in the world [15]. It belongs to the Semitic languages branching family of the Asian-African languages Group [4]. Of all the spheres of knowledge in the Arab heritage, cryptography has received the least attention from historians and researchers [14]. David Kahn stated that the cryptography was porn among the Arab [10]. Cryptography is used to achieve Authentication, Privacy/confidentiality, Integrity and Non-repudiation [3]. There are, in general, three types of cryptographic schemes: secret key (or symmetric) cryptography, public-key (or asymmetric) cryptography, and hash functions. Researchers in this field present many cryptography techniques depend on the three types of cryptography using different computer science techniques. There is a lack of using neural network in Arabic cryptography. This proposed study presents an Arabic cryptography algorithm using neural network.

Artificial neural networks (ANN) are simplified models of the central nervous system. They are networks of highly interconnected neural computing elements that have ability to respond to input stimuli. Researchers in cryptography field present many cryptography techniques the following section discusses the some of these works.

II. GENETIC ALGORITHMS:

Genetic algorithms1 (GAs) are a subclass of evolutionary algorithms where the elements of the search space G are binary strings (G = B) or arrays of other elementary types. As sketched in Figure 3.1, the genotypes are used in the reproduction operations whereas the values of the objective functions f ∈ F are computed on basis of the phenotypes in the problem space X which are obtained via the genotype-phenotype mapping “gpm”. [5], [8], [9]

Genetic Algorithm is applicable in many ways: State Assignment Problem Economics Scheduling Computer-Aided Design

III. NEURAL NETWORK

Artificial neural networks have motivated from their inception by the recognition that the brain computes in an entirely different way from the conventional digital computer. The brain contains billions of neurons with massive interconnections. Similarly, neural networks are massively parallel distributed processors that are made up of artificial neurons with interconnections. These are nonlinear dynamic machines which expand the expression of input data as a linear combination of inputs to synapses and then perform a nonlinear transformation to compute output [21]. Here are several strategies for learning: [6]


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1- Supervised Learning: Essentially, a strategy that involves a teacher that is smarter than the network itself. 2- Unsupervised Learning: Required when there isn’t an example data set with known answers.

Reinforcement Learning: A strategy built on observation. This research, Depends ON the first type

HEBBIAN NETWORK:

Discovered by Donald Hebb in 1949. The purpose of it is to modulation the weight matrix that correlation between nodes. Hebb introduced first base for training the neural network ( Hebbian learning Rule ) Which Adopted as basic rule for the development of encryption algorithms [19]. In this research use hebbian network Supervised. Where give code for each character. Network one features of this network IS Does not contain the Activation Function. When weight is modulation. Another feature of Hebbian learning for development is its automatic, self-organizing nature. This kind of learning can proceed simply in response to an array of inputs from the environment, without any consideration of what outputs should be produced in response to those inputs.[7]

NETWORK ARCHITECTURE:

Figure (1) shows the general network structure, which consists of a single layer explain the inputs and outputs

Figure(1) Hebbian Network architecture

To take the output of this neural network we apply the following

Equation: iWij Yj Yj = ∑ XiWij ………………………………… (1)

To modify the weights matrix we apply the following Equation

Wij(new) = Wij(old) + CXiYj……………………. (2) Where: Xi: input value i Yj: input value i Wij : weight matrix ij

C: Education rate ranging between (0<C≤1) The weight matrix is calculated using a genetic algorithm Instead of modifying the matrix equation weight to take the output required values.

GENETIC NEURAL NETWORK:

The genetic algorithm used in this research deals with the neural network weights Hebbian nerve to reach better results. Where traditional network training Hortamlah using the following characteristics:

1. Neural network consists of 12 knots in the input layer, and (20) a node in the output layer and there are no hidden layers, So we will have a matrix weights consisting of (144) weight - and that will be used chromosomes - were trained matrix weights this through the properties of mutations, crossover and selection. In order to get a correct matrix weights In order to get the matrix leads to Fitness Function. Fitness Function used in this research are referred to the equation (3)

FitnessFunction = @(x)curvefit(x,d)................. (3)

IV. RELATED WORKS:

Adnan Abdul-Aziz Gutub, etl. [1] Exploits the existence of the redundant Arabic extension character, i.e. Kashida. They proposed the pointed letters in Arabic text with a Kashida to hold the secret bit one un-pointed letters with a Kashida to hold zero. Their method can be classified under secrecy feature coding methods where it hides secret information bits within the letters benefiting from their inherited point.


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Prakash Kuppuswamy, Saeed Q Y Al-Khalidi [20] proposed new symmetric key algorithm using modular 37 and select any number and calculate inverse of the selected integer using modular 37. The symmetric key distribution should be done in the secured manner. Prakash Kuppuswamy, Yahya Alqahtani [18] proposed a modular 37 symmetric key algorithm in which an Arabic letters assigning to the integer value and the numerals 0-9 also assigned as an integer number called as synthetic value. The procedure of encryption and decryption is done by selecting random integer and calculate inverse of the selected integer with modular 37. The symmetric key distribution should be done in the secured channel for decrypting message. Khalid Alallayah, etl. In [11] introduced a Neuro-Identifier as a block box model simplified DES crypto systems. The goals of their model are to emulate construction the target cipher system and determine the key from given plaintext-cipher text pair. Mohammed Abdullah, etl. [12] Proposed and encryption algorithm based on simple multiplication and logarithm function with Caesar cipher. Their algorithm is adopted the coding of advanced language and is safe enough.

V. ENCRYPTION SYSTEM IN THIS SEARCH

Practical steps are summarized in the following two algorithms:

The first algorithm includes steps of encryption text. The second algorithm includes steps of decode the text by using Genetic algorithm

After applying encryption and decoding algorithm. Time is calculated for each case: ENCRYPTION:

1-Read file: Open file that located at the memory address ID Read file data and put it into the text file" in.txt" id = fopen('in.txt','r'); in=fscanf(id,'%s%[^\n]');

2-Extract the characters Convert the data file to a decimal value (ASCII CODE) Convert data file from a decimal value to a binary value of length 12 -bit and put it in variable cb. c=double (in); cb=de2bi(c,12);

3-Generate random integer numbers are not duplicated between 1 and 9 value of length 12– bit for each character (the encrypted message)

r = randi([1 9],m,12); [n,m]=size(c); m=number of rows n=number of charters with spaces

From this step we get code for any char (length 12 bit)

4-Train NN with Genetic algorithm Put the result in beam in one line w = []; Create loop of the number of text characters with spaces for i=1:m w= [w train_nnga(cb(i,:),r(i,:))]; End We get from this step beam in one line" w" contains the keys show blow:


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5- Writing letters:

Open text file "w.txt" id = fopen('w.txt','wt');

Print char i , its ASCII code and its encryption like this:

6- Write decryption message (The same way the previous step) 7-Decryption

Convert the file data from the binary value to an absolute decimal value Multiply the inverted matrix (w) with r Transported matrix (r ) to get outc ( Characters matrix after decoding ) outc=[ ]; for i=1:m outc=[outc bi2de(min(abs(round(inv(w(:,1+12*(i-1):i*12))*r (i,:)')), ones (12,1))')]; end

Write decryption massage (same way in step 5) -

Training network steps: -a genetic algorithm use Fitness Function to calculate the error FitnessFunction=@(x)curvefit(x,d);

The number of variables of genetic algorithm is =12*12=144 x is Weights get from genetic algorithm x=ga(FitnessFunction,numberOfVariables); -Reshape x by matrix w=reshape(x,12,12); y must equal zero until there is no error function y = curvefit(x,d)

y = 0; w=reshape(x,12,12); y = y+norm(d(:,2)-w*d(:,1));

end


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Finger [5]: Flowchart shows the encryption algorithm

Finger [6]: Flowchart shows the decryption algorithm


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VII. EXPERIMENTAL DESIGN:

The experiment is performed on a laptop core I5 , 2.5 GH. CPU with operating system windows 7. program performance metrics are collected: encryption and decryption time for any char and total time of encryption and decryption. Program written in matlab2014. The interface displays the following elements:

Plaintext size Decrypted text Text size Decrypted text size Time any letter( for encryption and decryption process) Total time ( for encryption and decryption process) Current weigh ( for encryption and decryption process) Current letter( for encryption and decryption process) Current code ( for encryption and decryption process) Encryption char time( for encryption and decryption process)

Figure [1], Figure [2] Figure [3] and Figure [4] shows the value of each element during and after the encryption and decryption.

Figure [1]: show encryption time and current weight of current letter

Figure [2]: show encryption text and total encryption time


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Figure [3]: show decryption time and current weight of current letter

Figure [4]: show decryption text and total decryption time

THE TIME TAKES FOR THE ENCRYPTION AND DECRYPTION: Time encryption calculated for any char is was (2900 ms) and time decryption for any char is (0.33 ms).It is noted that encryption time greater than the decoding time, because the network training and calculating weights is in the encryption stage while the decryption stage depends on applying of equation.

TABLE 1 SHOWS THE TIME OF IMPLEMENTATION OF DIFFERENT FILES SIZES.

SIZE OF TEXT (BYTE)

SIZE OF DECRYPTION TEXT

ENCRYPTION TIME(SEC)

DECRYPTION TIME(SEC)

TOTAL TIME FOR ENCRYPTION AND DECRYPTION

48 288 95 0.01783 95.18 102 612 189.93 0.012695 189.94 242 1452 521.63 0.0309 521.66 504 3024 916 0.118 916.11

Table (1) : the time of implementation of the encryption and decryption processes

VIII. RESULTS

1. Design Genetic neural network in order to encrypt texts, by training neural network weights using a genetic algorithm.

2. A neural network use in this research is consisted of 12 notes in input represent ascii code of char , and 12 notes in output are represent random numbers

3. Neural network using genetic algorithm , considered a matrix weights as chromosome of genetic algorithm to get the best results


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4. Encryption process by using genetic neural network includes high secret because of genetic algorithm accuracy. 5. Weights that get from genetic neural network training process be shared by the sender and the recipient. Where use

matrix weights in the encryption char ascii code process. recovery char's ascii code for Arabic letters .and use inverse matrix weights in the decryption process recovery char's ascii code

6. Genetic neural network in this research has been successful in the processes of encryption and decryption 100%

REFERENCES

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