12,643 research outputs found
An Empirical Investigation of Using ANN Based N-State Sequential Machine and Chaotic Neural Network in the Field of Cryptography
Cryptography 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 [1]. To overcome these drawbacks, we analyzed neural network is the best way to generate secret key. In this paper we proposed a very new approach in the field of cryptography. We are using two artificial neural networks in the field of cryptography. First One is ANN based n-state sequential machine and Other One is chaotic neural network. For simulation MATLAB software is used. This paper also includes an experimental results and complete demonstration that ANN based n-state sequential machine and chaotic neural network is successfully perform the cryptography
A Review on Biological Inspired Computation in Cryptology
Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research
Using Echo State Networks for Cryptography
Echo state networks are simple recurrent neural networks that are easy to
implement and train. Despite their simplicity, they show a form of memory and
can predict or regenerate sequences of data. We make use of this property to
realize a novel neural cryptography scheme. The key idea is to assume that
Alice and Bob share a copy of an echo state network. If Alice trains her copy
to memorize a message, she can communicate the trained part of the network to
Bob who plugs it into his copy to regenerate the message. Considering a
byte-level representation of in- and output, the technique applies to arbitrary
types of data (texts, images, audio files, etc.) and practical experiments
reveal it to satisfy the fundamental cryptographic properties of diffusion and
confusion.Comment: 8 pages, ICANN 201
Neural Cryptography
Two neural networks which are trained on their mutual output bits show a
novel phenomenon: The networks synchronize to a state with identical time
dependent weights. It is shown how synchronization by mutual learning can be
applied to cryptography: secret key exchange over a public channel.Comment: 9th International Conference on Neural Information Processing,
Singapore, Nov. 200
Grayscale Image Authentication using Neural Hashing
Many different approaches for neural network based hash functions have been
proposed. Statistical analysis must correlate security of them. This paper
proposes novel neural hashing approach for gray scale image authentication. The
suggested system is rapid, robust, useful and secure. Proposed hash function
generates hash values using neural network one-way property and non-linear
techniques. As a result security and performance analysis are performed and
satisfying results are achieved. These features are dominant reasons for
preferring against traditional ones.Comment: international journal of Natural and Engineering Sciences
(NESciences.com) : Image Authentication, Cryptology, Hash Function,
Statistical and Security Analysi
Interacting neural networks and cryptography
Two neural networks which are trained on their mutual output bits are
analysed using methods of statistical physics. The exact solution of the
dynamics of the two weight vectors shows a novel phenomenon: The networks
synchronize to a state with identical time dependent weights. Extending the
models to multilayer networks with discrete weights, it is shown how
synchronization by mutual learning can be applied to secret key exchange over a
public channel.Comment: Invited talk for the meeting of the German Physical Societ
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