Arabic Cryptography Technique Using Neural Network And Genetic Algorithm

International Research Journal of Computer Science (IRJCS) Issue 05, Volume 3 (May 2016)

ISSN: 2393-9842 www.irjcs.com

ARABIC Cryptography Technique Using Neural Network and Genetic Algorithm Shaza D. Rihan

Dr.Saif Eldin F.Osma

Omdurman Islamic University Research and Islamic world studies center Khartoum, Sudan

Emirates College for Science and Technology Computer science Department 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 AsianAfrican 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] _____________________________________________________________________________________________________ IRJCS: Impact Factor Value - Scientific Journal High Impact Factor value for 2014= 2.023 © 2014-16, IRJCS- All Rights Reserved Page -35

International Research Journal of Computer Science (IRJCS) Issue 05, Volume 3 (May 2016)

ISSN: 2393-9842 www.irjcs.com

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