Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.
About 7,724 papers for “Neural cryptography”
Protić, Danijela D. · Redalyc (Universidad Autónoma del Estado de México) · 2016
"Neural cryptography based on the tree parity machine (TPM) is presented in this paper. A mutual learning-based synchronization of two networks is studied. The training of the TPM based on the Hebbian, anti-Hebbian and random walk as well as on the secure key generation protocol is described. The most im portant attacks on the key generation process are shown."
Cited by 0
Ankesh Anand, Shmahell, Aly · Zenodo (CERN European Organization for Nuclear Research) · 2018
Neural Networks that invent their own encryption :key:
Cited by 0Open access
Vikas Thada, Utpal Shrivastava · International Journal of Computer Sciences and Engineering · 2018
International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Cited by 0Open access
Andreas Ruttor · ArXiv.org · 2007
Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.
Cited by 9Open access
Andreas Ruttor, Wolfgang Kinzel, Ido Kanter · Physical Review E · 2007
Synchronization of neural networks has been used for 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.
Cited by 65Open access
Alеxander Klimov, Anton Mityagin, Adi Shamir · Lecture notes in computer science · 2002
Cited by 131
Andreas Ruttor, Wolfgang Kinzel, Lanir N. Shacham et al. · Physical Review E · 2004
Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.
Cited by 48Open access
Borkovec, Tomáš · Brno University of Technology Digital Library (Brno University of Technology) · 2013
Bachelor's thesis aims to introduce neural networks and their properties, which can be used for creating cryptographic protocols based on neural networks. It proposed concrete use of neural networks in cryptography and creates a teoretical basis for the practical implementation. In practical section is discusses the creation of neural network on which we based cryptographic model. The constructed models are then subjected to testing. At the close of the thesis we discussed the advantages and disadvantages of possible use in real cryptographic modules.
Cited by 0
Andreas Ruttor, Wolfgang Kinzel, Ido Kanter · Journal of Statistical Mechanics Theory and Experiment · 2005
Neural cryptography is based on synchronization of tree parity machines by mutual learning. We extend previous key-exchange protocols by replacing random inputs with queries depending on the current state of the neural networks. The probability of a successful attack is calculated for different model parameters using numerical simulations. The results show that queries restore the security against cooperating attackers. The success probability can be reduced without increasing the average synchronization time.
Cited by 22Open access
Apdullah Yayık, Yakup Kutlu · Neural Network World · 2014
This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems.Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models.The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications.The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector.The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources.The global solar radiation forecasting is based on numerical weather prediction models.The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.
Cited by 39
Azraoui, Monir; Bahram, Muhammad; Bozdemir, Beyza; Canard, Sébastien; Ciceri, Eleonora; Ermis, Orhan; Masalha, Ramy; Mosconi, Marco; Önen, Melek; Paindavoine, Marie; Rozenberg, Boris; Vialla, Bastien; Vicini, Sauro · Graduate School and Research Center in Digital Science (EURECOM)
Cited by 0
Wolfgang Kinzel, Ido Kanter · Advances in Solid State Physics · 2007
Cited by 85
Andreas Ruttor, Wolfgang Kinzel, Ido Kanter · 2005
Cited by 0
Satish Kumar Singh · International Journal for Research in Applied Science and Engineering Technology · 2019
CONCLUSION AND FUTURE WORK Multilayer progress perc back-propagation (BP) has been found cryptographic applications that use fixe classic cryptography. The length structure of the neural network. Th the algorithms worked well on the image the network has not been trained. The it was not influenced by geometric image, as translation, scale, an network has proved suitable for images, such as satellite images, w at least 98%. The reconstructed goodness of adaptation better than that of me and each took about 1.6 minutes important to mention here that the satellite images. For future work, efficiency the proposed algorithms will be ev randomness measures, and the pr compared to the traditional image.
Cited by 2Open access
Lanir N. Shacham, Einat Klein, Rachel Mislovaty et al. · Physical Review E · 2004
A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.
Cited by 57Open access
Andreas Ruttor, Wolfgang Kinzel, Rivka Naeh et al. · Physical Review E · 2006
Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.
Cited by 79Open access
Chen, Abel C. H. · arXiv (Cornell University) · 2024
In recent years, quantum computers and Shor quantum algorithm have posed a threat to current mainstream asymmetric cryptography methods (e.g. RSA and Elliptic Curve Cryptography (ECC)). Therefore, it is necessary to construct a Post-Quantum Cryptography (PQC) method to resist quantum computing attacks. Therefore, this study proposes a PQC-based neural network that maps a code-based PQC method to a neural network structure and enhances the security of ciphertexts with non-linear activation functions, random perturbation of ciphertexts, and uniform distribution of ciphertexts. In practical experiments, this study uses cellular network signals as a case study to demonstrate that encryption and decryption can be performed by the proposed PQC-based neural network with the uniform distribution of ciphertexts. In the future, the proposed PQC-based neural network could be applied to various applications.
Cited by 0Open access
Tao Dong, Tingwen Huang · IEEE Transactions on Neural Networks and Learning Systems · 2019
Neural cryptography is a public key exchange algorithm based on the principle of neural network synchronization. By using the learning algorithm of a neural network, the two neural networks update their own weight through exchanging output from each other. Once the synchronization is completed, the weights of the two neural networks are the same. The weights of the neural network can be used for the secret key. However, all the existing works are based on the real-valued neural network model. There are seldom works studying the neural cryptography based on a complex-valued neural network model. In this technical note, a neural cryptography based on the complex-valued tree parity machine network (CVTPM) is proposed. The input, output, and weights of CVTPM are a complex value, which can be considered as an extension of TPM. There are two advantages of the CVTPM: 1) the security of CVTPM is higher than that of TPM with the same hidden units, input neurons, and synaptic depths and 2) the two parties with the CVTPM can exchange two group keys in one neural synchronization process. A series of numerical simulation experiments is provided to verify our results.
Cited by 151
Arindam Sarkar · Applied Intelligence · 2021
Cited by 4
Nankun Mu, Xiaofeng Liao · Lecture notes in computer science · 2013
Cited by 14