Authors: Dr. Amol Dhakne, Prasann Shimpi, Sanket Halake, Shivam Dharmshetti, Shashank Singh
DOI Link: https://doi.org/10.22214/ijraset.2022.42522
Certificate: View Certificate
Neural Cryptography is a new thread that integrates cryptography and neural networks for cryptanalysis and encryption applications. We show that Neural Networks can execute symmetric encryption in an adversarial context in this paper, and we build on the existing literature on the subject. Cryptography\'s purpose is to make it difficult to decipher a cypher and recreate the plain text without the associated key. Your messages are encrypted with excellent cryptography in such a way that brute force attacks against the algorithm or key are nearly impossible. Good cryptography is secure because it employs extremely long keys and encryption techniques that are resistant to various types of attack. The neural net application is the next step in the evolution of good cryptography. This paper discusses the use of neural networks in cryptography, including how to create neural networks that can be utilized in cryptography.
I. INTRODUCTION TO CRYPTOGRAPHY
The creation of new security methods that safeguard anyone from trespasser reading is the focus of cryptography. Data privacy systems are referred to as "cypher systems." The cypher key is a set of rules that are used to encrypt all data. The process of transforming open text, such as a message, into encrypted text using rules is known as encryption. Cryptanalysis of the news is the inverse technique, in which the recipient of the encryption converts it back to the original text. The cypher key must comprise a number of crucial qualities. The best illustration is the singularity of encryption and cryptanalysis. Letters, digits, and punctuation marks in the international alphabet are all the same. All the while with mystery of data the propensity for perusing the code news without realizing the code key was advanced. Code keys were observed intently. The primary objective of cryptology is to figure the code news and to recreate the utilized keys with the assistance of good examination of code news. It utilizes numerical insights, variable based math, numerical phonetics, and so on, just as realized slipups made by figures as well. The legitimateness of the open text and the applied code key are reflected in each code framework. Further developing the code key assists with diminishing this legitimacy. The wellbeing of the code framework lies in its insusceptibility against interpretation.
The objective of cryptanalysis is to make it conceivable to take a code message and imitate the first plain message without the comparing key. Two significant strategies utilized in encryption are symmetric and hilter kilter encryption. In symmetric encryption, two gatherings share a solitary encryptionunscrambling key (Khaled, Noaman, Jalab 2005). The sender scrambles the first message (P), which is alluded to as plain message, utilizing a key (K) to create an obviously irregular hogwash, alluded to as code message (C), i.e.:[4]
C = Encrypt (K,P)
When the code text is created, it can be sent. Upon receipt, the code text can be changed back to the first plain text by utilizing a decoding calculation and the very key that was utilized for encryption, which can be communicated as follows:
P = Decrypt (K,C)
In asymmetric encryption, two keys are utilized, one key for encryption and one more key for unscrambling. The length of a cryptographic key is quite often estimated in bits. The more pieces that a specific cryptographic calculation permits in the key, the more keys are conceivable and the safer the calculation becomes.[4]
A. Introduction to Symmetric Encryption
Symmetric encryption is a type of encryption in which the sender and receiver use the same key to encrypt and decrypt plaintext and ciphertext, respectively.[4] Block or stream cyphers have traditionally been employed in symmetric encryption algorithms. It has been shown, however, that with endtoend adversarial training, a system of neural networks may learn how to conduct types of 'encryption' and 'decryption' without the usage of a specific cryptographic algorithm.[4]
???????B. Introduction to Artificial Neural Networks
A neural organization is an organization or circuit of neurons, or in this day and age, a counterfeit neural organization consisting of fake neurons or hubs.[1] Along these lines, a neural organization can be either natural (composed of natural neurons) or counterfeit (composed of fake neurons) and used to tackle computerized reasoning (AI) challenges. Counterfeit neural organizations model the associations of natural neurons as loads between hubs. An excitatory connection has a positive weight, while inhibitory associations have a negative weight. A weight is applied to all contributions before they are added. A direct mix is the name for this action.Finally, the yield's sufficiency is constrained by actuation work.[1][2] For example, an adequate yield range is ordinarily somewhere in the range of 0 and 1, in spite of the fact that it may likewise be somewhere in the range of 1 and 1.
II. LITERATURE SURVEY
Sr. No 
Paper Title 
Authors & Published on 
Methodology 
1 
Deep Neural Networks based key concealment schemes 
Taehyuk Kim, Tae Young Youn 04Nov2020 
In this paper, we propose a new DNNsbased key con cealment scheme for concealing cryptographic keys within DNNs. 
2 
A Neural Network based Approach for Cryptographic Function Detection 
Li Jia, Anmin Zhou, Peng Jia, Luping Liu, Yan Wang, Liang Liu 2020 
This paper proposed a novel approach for cryptographic function detection in malware. 
3 
Neural Cryptography based on Complex Valued Neural Networks 
Tao Dong,Tingwen Huang 2019 
This paper took a complex value based parity machine (CVTPM) approach to neural cryptography. 
4 
Neural Cryptography Based on Topology Evolving Neural Networks 
Yuetong Zhu, Danilo Vasconcellos Vargas, Kouichi Sakurai 2018 
This paper suggested a neural network architecture to learn neural cryptography. 
5 
Neural Cryptography: From symmetric encryption to Adversarial Steganography 
Dylan Modesitt, Tim Henry, Jon Coden, and Rachel Lathe 2018 
This paper discussed various research in the field of neural cryptography from symmetric encryption to steganography. 
6 
Use of Neural Networks in Cryptography: A Review 
Pranita P. Hadke, Swati G. Kale 2016 
This paper gave a review of the current usage and possibilities of the utilization of neural networks in cryptography. 
7 
Neural Network based Cryptography 
Apdullah Yayik,, Yapik Kutlu 2014 
This paper discussed the iteration of ANNs in cryptography and their advantages over other methods (symmetric encryption). 
8 
Cryptography based on Neural Networks 
Eva Volna, Michael Janosek, Martin Kotyrba 2014 
This paper brought into light the method of practical implementation of neural cryptography in real world applications. 
9 
Cryptography using Artificial Neural Networks

Vikas Gujral, Satish Pradhan 2009 
This paper represented a comparative study between different neural network architectures for an adder and discussed their possible applications in cryptography. 
10 
Neural Cryptography 
Wolfgang Kiesel,Ido Kanter. 2002 
This paper discussed the first iteration of ANNs in cryptography and their advantages over other methods (symmetric encryption). 
1. A Neural Network based Approach for Cryptographic Function Detection:
They suggested an unique neuralnetbased technique for identifying cryptographic features in malware in this research, and we constructed a prototype system. Their design is made up of two key parts: Instruction2vec, which extracts the semantic information from assembly instructions and converts it into 100dimensional vectors, and an enhanced neural network. KMaxCNN Calculate function embeddings and complete the process of categorizing cryptographic functions with care.
2. Deep Neural Networks based key concealment schemes
To keep the Internetofthings (IoT) environment secure, employing a cryptographic function to various IoT devices has become vital. An important factor to consider is how to store a cryptographic key (or passwords) securely. A popular method is to store the key in the storage protected by some hardwarebased security functions. This paper presents a novel concept to conceal cryptographic keys into deep neural networks (DNNs), named DNNsbased key concealment scheme. In this scheme, a key can be concealed into a proper deep neural network model which is trained with secret input data. We demonstrate the practical applicability of our concept by presenting an instance and a usecase scenario of the DNNsbased key concealment scheme and show its correctness. To prove its robustness, two fundamental security evaluation methods are proposed for investigating the security of the instantiation. To the best of our knowledge, this is the first attempt of its kind.
3. Neural Cryptography based on Complex Valued Neural Networks
A public key exchange technique based on the notion of neural network synchronization is known as neural cryptography. The two neural networks adjust their own weight by sharing output from each other using a neural network's learning method. The weights of the two neural networks are the same after the synchronization is complete. The secret key may be created using the neural network's weights. All existing research, on the other hand, is based on the realvalued neural network paradigm. Rarely are papers on neural cryptography based on a complexvalued neural network model published. A neural cryptography based on the complexvalued tree parity machine network is presented in this technical note.
4. Neural Cryptography Based on Topology Evolving Neural Networks
Mathematical theory is used to build modern encryption methods. Recent research demonstrates a new approach in cryptography based on neural networks. A cryptographic scheme is developed automatically rather than knowing a specific technique. While one type of neural network is employed to implement the method, it is uncertain if the notion of neural cryptography can be accomplished using other neural network design. This attribute is used in this study to develop a neural cryptography scheme based on the Spectrumdiverse unified neuroevolution architecture, a novel topology changing neural network architecture.
5. Neural Cryptography: From symmetric encryption to Adversarial Steganography
Neural Cryptography is an emergent field that aims to combine cryptography with Neural Networks for applications in cryptanalysis and encryption. They (1) demonstrate that Neural Networks may perform symmetric encryption in an adversarial scenario and improve on the existing research on the subject in this study.
They also (2) demonstrate that by putting Neural Networks through known cryptographic games based on Ciphertext Indistinguishability, they may discover known cryptographically unsafe communication. Finally, they (3) discuss more work in Neural Steganography, including building neural endtoend steganographic (imageinimage, textinimage, videoinvideo) algorithms in the face of adversarial networks seeking to censor.
6. Use of Neural Networks in Cryptography: A Review
Secret information is made illegible for unauthorized users using cryptography. Many cryptographic methods exist, but they are more complicated procedures that demand more processing capacity. This study examines how neural networks aid cryptography and how neural networks and cryptography may be used together to improve security.
7. Neural Network based Cryptography
The use of neural networks for cryptography is demonstrated in this study. There are two steps to the system. In the first stage, neural networkbased pseudorandom numbers (NPRNGs) are generated, and the outputs are checked for randomness using randomness tests developed by the National Institute of Standards and Technology (NIST).
8. Cryptography based on Neural Networks
The neural net application is the next step in the evolution of excellent cryptography. This paper discusses the use of neural networks in cryptography, including how to create neural networks that can be utilized in cryptography. An experimental demonstration is also included in this publication.
9. Cryptography using Artificial Neural Networks
Different neural network topologies for an Adder are compared, and their benefits and drawbacks are highlighted. A finite state sequential machine was successfully developed using a Jordan (Recurrent network) trained using the backpropagation technique. The resulting sequential machine was utilized for encryption, with the initial key serving as the decryption key. A chaotic neural network with weights determined by a chaotic sequence was also used to accomplish cryptography.
10. Neural Cryptography
The first iteration of ANNs in cryptography was addressed in this study, as well as its advantages over other approaches (symmetric encryption)
III. PROPOSED MODEL
The proposed project aims to create three neural networks, Alice, Bob and Eve. Alice and Bob participate in a conversation protected by symmetric encryption. Simultaneously, Eve eavesdrops the conversation. As a result both the receiver and Eve have access to the ciphertext. However, the receiver does have the associated nbit key. On the other hand, Eve doesn’t have the associated key. While the receiver deciphers the cipher text, Eve does try to decrypt without the associated key.[3][5]
???????A. Dataset
The dataset consists of two equally sized randomly generated strings: one as the plaintext and the second that functions as its associated keys. A uniform random distribution is achieved for this purpose. The generated keys and plaintext have the same length of N bits. The lengths may be 16, 32 or 64 bits. The length may be picked randomly out of the three values.
???????B. Artificial Neural Networks
Artificial neural networks consist of a pool of simple processing units that communicate with each other by sending signals to each other over a number of weighted links. The set of key aspects of ANN's is:
The activation state yk of each unit, corresponding to the output of units. connections between units. In general, each connection is defined by the weight wjk . This determines the effect of the signal on unit j on unit k. Propagation rule that determines the effective input sk of the unit from the external input. Activation function Fk that determines the new activation level based on the valid input sk (t) and the current activation yk (t) (ie update).[6][7][8]
External input of each unit (also called bias or offset) θk.
The figure above depicts the basic structure of an artificial neural network and its different components. For the purpose of this project, we construct three neural networks acting as sender, receiver and adversary respectively[6][7][8]. This proposed model, for the purpose of demonstration, includes four layers that make the structure of the convolutional neural networks[4].
???????C. Convolutional Neural Networks
A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning system that can take an input image, assign relevance (learnable weights and biases) to various aspects/objects in the image, and distinguish between them. When compared to other classification methods, the amount of preprocessing required by a ConvNet is significantly less. While basic approaches require handengineering of filters, ConvNets can learn these filters/characteristics with enough training.
CNN encryption's robustness against crypto assaults was not examined, because it is beyond the scope of this research. The final results show how well a particular neural network may be used for symmetric cryptography[11]. For the purpose of demonstration in this context, there are three neural networks. They are as follows:
The purpose of each layer is studied later on in this paper.
2. Bob: Consider Bob as the neural network as the receiver of the message. The architecture of Bob is an exact replica of Alice’s. It consists of a fully connected layer, followed by three sigmoid convolutional layers and a nonlinear tanh layer. The input to Bob, however, is the nbit cipher text generated by the sender, in this case Alice, and the key used for the symmetric encryption. All layers perform the same operation as Alice and the output produced is the deciphered text that is the same as the one that was the input to Alice. This process concludes the last stage of symmetric encryption.[10]
3. Eve: In the scenario in consideration, Eve is the adversary, performing a maninmiddle attack.Traditionally, a maninthemiddle would be able to view messages in transit by eavesdropping through the network. By using machine learning, the neural networks are able to learn cryptooperations and thus eliminates the need for exchanging keys with every message. Here, the architecture of the adversary networks is the same as the others. The inputs to this ANN, however, is only the ciphertext (an identity string is also passed along with the cipher text). This ANN attempts to crack the ciphertext without the use of the secret key known only to Alice and Bob. The primary purpose of this is to test the strength and reliability of using ANNs for symmetric encryption while saving human effort.[10]
???????D. Proposed Neural Network Architecture
The proposed architecture of each ANN consists of four layers. They are as follows:
2. Sigmoid Layer: The sigmoid function is a type of logistic function that is commonly symbolised by the o(x) or sig (x). It is provided by:
σ(x) = 1/(1+exp(x))
In neural networks, the sigmoid function is utilized as an activation function. An activation function is used to pass a weighted sum of inputs through, and the output is used as an input to the following layer. When a neuron's activation function is a sigmoid function, the output of this unit will always be between 0 and 1. The output of this unit would also be a nonlinear function of the weighted sum of inputs, as the sigmoid is a nonlinear function. A sigmoid unit is a type of neuron that uses a sigmoid function as an activation function[12].
3. Tanh Layer: The Tanh (also "tanh" and "TanH") function is another name for the hyperbolic tangent activation function. It resembles the sigmoid activation function in appearance and even has the same Sshape. The function accepts any real value as input and returns a value between 1 and 1. The larger the input (more positive), the closer the output to 1.0, and the smaller the input (more negative), the closer the output to 1.0.
This is how the Tanh activation function is calculated:
(e^x – e^x) / (e^x + e^x)
Where e is the base of the natural logarithm and is a mathematical constant[13].
???????E. Encrypting and Decrypting Images
The model proposed above works only for text messages. In order to work with files and media content like images, this proposes a combined implementation of chaos maps and the model proposed above. Chaotic theory is a branch of mathematics that studies the dynamic behavior of natural and manmade systems that are affected by initial conditions such as weather, climate, and road traffic. It can be investigated using a chaotic mathematical model, as well as recurrence plots and Poincare maps. Emerging technologies such as neurology, cardiology, control and circuit theory, weather prediction, and others use chaos theory. Chaos is defined as "when the present determines the future, yet the approximate present cannot approximate the future." In chaos, even little changes in the original conditions can result in a sequence that is completely unrelated. For our purpose we have used logistic chaotic maps.[14]
Mathematically, the logistic map is represented using the equation
f (x) = rx(1 − x)
xn+1 = f (xn ) …(1)[14].
Here, xn depicts the chaotic sequence ranging between 0 and 1 as illustrated in Figure 5.
Initially, the map is in the xn=0 = x0 ∈[0,1] state. The variable r takes a value between 0 and 4.When r is between 0 and 1, xn = 0 regardless of the beginning conditions x0. When r is between 1 and 3, the value of xn stabilizes at (r 1) r, regardless of the initial conditions x0. When r is between 3 and 1+ 6 (about 3.45), the value of xn oscillates between two values for as long as r is between 3 and 1+ 6 (roughly 3.45)[14]. The value of xn oscillates between four values forever when r is between 3.45 and 3.54 (roughly). The value of xn oscillates between 8 values when r is somewhat larger than r, then 16, 32, and so on. The commencement of chaos occurs at roughly 3.57 r, at the end of the period doubling cascade[14]. Small changes in the starting state produce significantly different effects over time in this region, which is a key feature of chaos. Beyond 3.57, the behavior is chaotic, but there are isolated values of r that appear to be nonchaotic; these are commonly referred to as islands of stability.[14].The logistic map is a onedimensional discretetime map with an astonishing level of complexity despite its formal simplicity. It was historically one of the most important and archetypal systems in the early days of deterministic chaos study[15].
2. Image Encryption
There are three major processes in the encryption algorithm. The chaotic sequences are generated in the first stage. To create the requisite encrypted image, the second stage confused the pixel values, and the third step shuffled the pixel position. Let f be a picture with the dimensions M N. The pixel of f is represented by f(i,j), where i and j are in the range of 1<=i<=M and 1<=j<=N. The grey value at the pixel position (i, j) of the picture f is now denoted by f(i,j). The logistic map's initial condition is derived from a 256bit (32character) secret key written in ASCII as K = K1K2 K3......K32 (Ki signifies the 8bit key character in the ith key position).[14]
The step by step procedure for logistic encryption using Chaos Maps is as follows[14]:
Step 1: Convert the picture of size M N pixels into an array of P = P,P,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L,P,L Then, using the mod operation, transform the pixel values to an unsigned integer in the range of 0 to 255.The initial values of the chaos map is recalculated after every pixel encryption based on the previous encryption value as well as the key value.
Step 2: With the initial condition x0 and the parameter r = 3.999, generate n number of chaotic sequences xi ={x1,x2,x3,L,xn} in the range 0 to 1 using the logistic map mentioned in Eq. (1). Then, using the mod operation, turn xi into an unsigned integer in the range of 0 to 255.
Step 3: To confuse iii the pixel value, generate the sequence C = P x. The bitwise XOR operation is indicated by the symbol.
Step 4: To get the image f′, convert Ci = {C1,C2,C3,L,Cn} to an array of size MN. Then, to get X, add one to the unsigned integer sequence xi = {x1, x2, x3,L, xn} and convert it to an array of size M N.
Step 5: Finally, perform the pixel shuffling procedures below to obtain the needed encrypted image f. The values of j and k range from 1 to 255. The sign indicates that the values of two pixel positions of f ′ are swapped.
f′(X(j,j),k)⇔ f′(X(j+1,j+1),k)
f′(k,X(j,j))⇔ f′(k,X(j+1,j+1)). (4)
The final encrypted image is now f.
3. Image Decryption
The process of decrypting an image is the inverse of encryption.
4. Proposed Methodology for Imagebased Cryptography:
For the purpose of implementing image encryption in our demonstration, we use logistic encryption with key mixing. The generation of the chaos sequence is according to the steps mentioned above but each time a new sequence is generated, it is done with respect to the previous sequence and the key.
The key required for logistic encryption is a string in which only a fixed number of characters are implemented for the purpose of cryptography. This key is then encrypted using the ANN for encryption, in this case Alice, and thus the encrypted key along with the cipher image is transmitted.
The process of decryption is the reverse of the above process. The encrypted key is first decrypted using the decryption ANN model, in this case Bob. On obtaining the key for image decryption, the “plain” image is then obtained from the encrypted image.
???????F. Advantages
If successfully implemented, this method will have the following advantages:
???????G. Limitations
???????H. Future Possibilities
As huge advances are observed in the field of big data and machine learning, it is obvious that there will be practical implementations of the proposed model with a lot of tweaks and improvements. Furthermore, this paper only discusses symmetric encryption using machine learning and ANNs. Even stronger and reliable are asymmetric encryption algorithms like RSA, etc. which are yet to have an implementation through machine learning. As predicted with symmetric encryption, it is no longer a point to ponder upon that ANNs will supersede the traditional cryptographic functions, provided there is availability of the huge pool of resources and computing power required to do the same. As of writing this paper, there are a few systems capable of performing neural cryptographic operations but steady advantages are being made at a neverseenbefore pace in computing power and high performance computing. If it is successfully implemented and made feasible for everyday applications, a drastic decrease in maninthemiddle attacks will be imminent.
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Copyright © 2022 Dr. Amol Dhakne, Prasann Shimpi, Sanket Halake, Shivam Dharmshetti, Shashank Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors : Prasann Shimpi
Paper Id : IJRASET42522
Publish Date : 20220511
ISSN : 23219653
Publisher Name : IJRASET
DOI Link : Click Here