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Neural Cryptography for Secret Key Exchange International Journal for Modern Trends in Science and Technology Volume: 03, Issue No: 03, March 2017 ISSN: 2455-3778 http://www.ijmtst.com Neural Cryptography for Secret Key Exchange Prof.Pranita P.Hadke1| Prof.Madhuri R. Dubey2 1 Department of Information Technology, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India. 2 Department of Information Technology, SBJITMR, Nagpur, Maharashtra, India To Cite this Article Prof.Pranita P.Hadke and Prof.Madhuri R. Dubey, “Neural Cryptography for Secret Key Exchange”, International Journal for Modern Trends in Science and Technology, Vol. 03, Issue 03, 2017, pp. 15-18. ABSTRACT The goal of any cryptography system is the exchange of information among the intended user without any leakage of information to other who may have unauthorized access to it. A common secret key could be created over a public channel accessible to any opponent. Neural networks can be used to generate common secret key. In case of neural cryptography, both the communicating networks receive an identical input vector, generate an output bit and are trained based on the output bit. The two networks and their weights vectors exhibit a new phenomenon, where the networks synchronize to a state with identical time-dependent weights. The generated secret key over a public channel is used for encryption and decryption of the message or information send over the channel. KEYWORDS: Neural networks, Cryptography, Key exchange, neural cryptography. Copyright © 2017 International Journal for Modern Trends in Science and Technology All rights reserved. decryption. These keys can be identical as a public I. INTRODUCTION and a private key. Nowadays information security has become an Key generation is the most significant issue in important aspect in every organization. In other cryptography technique. In recent times wide words people have to be assured that the ranges of techniques are developed to protect data information to be read by only the sender and and information from eavesdropper. These receiver. The basic need to provide security is using algorithms have their virtue and shortcomings. A cryptography. Cryptosystem are commonly used neural network based approach offers an attractive for protecting the integrity, confidentiality, and solution to this problem in that it provides a authenticity of information resources. The design suitable framework within which data can be of cryptography algorithm is complicated by the readily code. Neural networks are non linear fact that a variety of cryptanalytic attacks are statistical data modeling tools. They can be used to available that can often be successfully used to model complex relationship between inputs and recover the key or the plaintext. outputs or to find patterns in data. A phenomenon Cryptography has two style of encrypting data; of neural networks is applied in cryptography symmetrical and asymmetrical. Symmetric system. This is used for generating secret key. encryptions use the same key for encryption and decryption process, and also can be defined as a II. PROBLEM OF SECRET KEY EXCHANGE IN CRYPTOGRAPHY secret-key, shared-key, and private-key. Asymmetric cryptography uses different encryption The problem of key exchange is one of the main keys for encryption and decryption process. In this concerns of classical cryptography and it was case an end user on a network, public or private, extensively studied in classical cryptography. The has a pair of key; one for encryption and one for first published key exchange protocol was based on number theory and it is known by Diffie-Hellman 15 International Journal for Modern Trends in Science and Technology Prof.Pranita P.Hadke and Prof.Madhuri R. Dubey : Neural Cryptography for Secret Key Exchange key exchange protocol. While it depends on the coupled to the two system. The networks received a difficulties of computing discrete logarithms, it is common input vector after calculating their output vulnerable to man-in-middle attack. Moreover, it is and updated their weight vector according to the computationally intensive. The man-in-middle match between their mutual outputs in every time attack is solved by authentication mechanisms. step. The input and output relations are not exchanged through a public channel until their III. RELATED WORK weight vectors are matched and can be used as a This paper [1] proposed two artificial neural secret key for encryption and decryption of secret networks for cryptography. Experimental results message. The weight vectors of the two neural show that the two networks are secure, without networks begin with random number, which are any result about efficiency. generated by Pseudo-Random Number Generator This paper [2] presented synchronization (PRNGs). The proposed model fixed the security neural key-exchange algorithm for cryptography. against numerical attacks. The model has multi-layer feed-forward neural This paper [6] proposed a secret key over a network which have two tree parity machine (TPM) public channel using artificial neural network. The that synchronized with a random initial weight act artificial neural network contains of two multi layer as common secret key for the encryption and neural networks trained on their mutual output decryption process. The weight can be updated bits and able to synchronize. The two network according to the learning rules only if the output starting from random initial weights and learning values of the two machines are equal. Throughout from each other with two multilayer networks relax the synchronization process, only the input vectors to the state with time dependent identical synaptic and the output vectors are transmitted over the weights. The partners didn’t exchange any public channel. information over a secret channel before their This paper [3] presented a new modification of communication. Synchronization of neural the Advanced Encryption Standard to be immune networks can be considered as the key generation against some attacks using non linear neural in cryptography. The common identical weights of network. The neural network model performs the two partners can be used as a key for key cryptography processes via a symmetric key cipher generation over public channels which are not that used as the initial weights for the neural based on number theory. Experimental result network which trained to its final weight fast and shows that the model is fast, simple, and secure. low cost algorithm. The objective form the network This paper [7] presented a secured has been selected to equivalent the output of the cryptography secret-key based on neural network AES that have an efficient and recommended in a public channel. The proposed model has two security. Simulation results show the proximity of neural network that are trained on their alternative the result accomplished by the proposed NN-based output synchronized to an equal time dependent AES cryptosystem with that of the normal AES. weight vector through a chaos synchronization This paper [4] proposed multi-layer neural system that starting from different initial condition. networks in cryptography. The multilayer neural The system combined the neural network with the networks in cryptography. The multilayer neural logistic chaotic map. The both partners used their networks modified by back-propagation. The neural networks as input for the logistic maps planned model converted the input message into which generated the output bits to be learned, by ASCII code then gets the sequence of bits for each mutually learning. The two neural networks code which divides into 6 bit blocks are used as approach each other and generated a matching input for the encryption process. The cipher key is signal to the chaotic maps. The chaotic the neural network structure contained input synchronization applied in the neural layer, hidden layer, output layer, and updated cryptography enhanced the cryptography system weights. Experimental results show that the and improved the security. system is secure. This paper [8] proposed a common secret key This paper [5] proposed a secret key using generated based on neural networks. The neural neural cryptography, based on synchronization of cryptography has two communication networks Tree Parity Machine (TPMs) by mutual learning. that received an identical input vector, generated The system has two identical dynamical system, an output bit and are trained based on output bit. which starting from different initial conditions and The network model initials the weight randomly synchronized by a common input values which are and the outputs bit are generated finally and 16 International Journal for Modern Trends in Science and Technology Prof.Pranita P.Hadke and Prof.Madhuri R. Dubey : Neural Cryptography for Secret Key Exchange exchange between partners. The weight may be modified if the outputs of both partners are matched. The modified weight after synchronize act as a secret key for the encryption and decryption process. Simulation results show that the cryptosystem based on ANNs is secure. In this paper [9] a secret key is generated over a public channel based on neural network. The model has a neural network machine contains of two partners started with initial weights and Tree Parity Machine different initial condition which synchronized by a common external signal and received a common 3. Tree Parity Machine random input vector and learned their mutual output bits. The synaptic weights are used as a Tree Parity Machine is special type
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