12,860 research outputs found
Dynamics of neural cryptography
Synchronization of neural networks has been used for novel public channel
protocols in cryptography. In the case of tree parity machines the dynamics of
both bidirectional synchronization and unidirectional learning is driven by
attractive and repulsive stochastic forces. Thus it can be described well by a
random walk model for the overlap between participating neural networks. For
that purpose transition probabilities and scaling laws for the step sizes are
derived analytically. Both these calculations as well as numerical simulations
show that bidirectional interaction leads to full synchronization on average.
In contrast, successful learning is only possible by means of fluctuations.
Consequently, synchronization is much faster than learning, which is essential
for the security of the neural key-exchange protocol. However, this qualitative
difference between bidirectional and unidirectional interaction vanishes if
tree parity machines with more than three hidden units are used, so that those
neural networks are not suitable for neural cryptography. In addition, the
effective number of keys which can be generated by the neural key-exchange
protocol is calculated using the entropy of the weight distribution. As this
quantity increases exponentially with the system size, brute-force attacks on
neural cryptography can easily be made unfeasible.Comment: 9 pages, 15 figures; typos correcte
IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA
Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG) have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN). The Artificial Neural Networks (ANN) providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation) to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA)
Artificial intelligence and quantum cryptography
The technological advancements made in recent times, particularly in artificial intelligence (AI) and quantum computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the ‘quantum threat’. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area
Public channel cryptography by synchronization of neural networks and chaotic maps
Two different kinds of synchronization have been applied to cryptography:
Synchronization of chaotic maps by one common external signal and
synchronization of neural networks by mutual learning. By combining these two
mechanisms, where the external signal to the chaotic maps is synchronized by
the nets, we construct a hybrid network which allows a secure generation of
secret encryption keys over a public channel. The security with respect to
attacks, recently proposed by Shamir et al, is increased by chaotic
synchronization.Comment: 4 page
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
Modifying Hebbian Network for Text Cipher
The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information.
This work modernize the feedforward neural network, so the secret message will be encrypted by unsupervised neural network method to get the cipher text that can be decrypted using the same network to get the original text.
The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding lengths.
In this work, the key is the final weights that are obtained from the learning process within the neural network stage, So the work can be represented as an update or development for using the neural network to enhance the security of text.
As a result for a powerful design, the resulted cipher system provides a high degree of security which satisfies the data confidentially which is the main goal of the most cryptography systems
Authenticated tree parity machine key exchange
The synchronisation of Tree Parity Machines (TPMs), has proven to provide a
valuable alternative concept for secure symmetric key exchange. Yet, from a
cryptographer's point of view, authentication is at least as important as a
secure exchange of keys. Adding an authentication via hashing e.g. is
straightforward but with no relation to Neural Cryptography. We consequently
formulate an authenticated key exchange within this concept. Another
alternative, integrating a Zero-Knowledge protocol into the synchronisation, is
also presented. A Man-In-The-Middle attack and even all currently known
attacks, that are based on using identically structured TPMs and
synchronisation as well, can so be averted. This in turn has practical
consequences on using the trajectory in weight space. Both suggestions have the
advantage of not affecting the previously observed physics of this interacting
system at all.Comment: This work directly relates to cond-mat/0202112 (see also
http://arxiv.org/find/cond-mat/1/au:+Kinzel/0/1/0/all/0/1
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
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