Biomedical subjects

Ido Kanter

Publications and source records attributed to Ido Kanter.

7 recordsLinked to original sources

PubMed2004

Cooperating attackers in neural cryptography.

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.

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PubMed2004

Neural cryptography with feedback.

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.

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PubMed2003

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.

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PubMed2002

Mutual learning in a tree parity machine and its application to cryptography.

Mutual learning of a pair of tree parity machines with continuous and discrete weight vectors is studied analytically. The analysis is based on a mapping procedure that maps the mutual learning in tree parity machines onto mutual learning in noisy perceptrons. The stationary solution of the mutual learning in the case of continuous tree parity machines depends on the learning rate where a phase transition from partial to full synchronization is observed. In the discrete case the learning process is based on a finite increment and a full synchronized state is achieved in a finite number of steps. The synchronization of discrete parity machines is introduced in order to construct an ephemeral key-exchange protocol. The dynamic learning of a third tree parity machine (an attacker) that tries to imitate one of the two machines while the two still update their weight vectors is also analyzed. In particular, the synchronization times of the naive attacker and the flipping attacker recently introduced in Ref. 9 are analyzed. All analytical results are found to be in good agreement with simulation results.

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PubMed2002

Generalization and capacity of extensively large two-layered perceptrons.

The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, alpha(c), at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different.

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PubMed2002

Learning and predicting time series by neural networks.

Artificial neural networks which are trained on a time series are supposed to achieve two abilities: first, to predict the series many time steps ahead and second, to learn the rule which has produced the series. It is shown that prediction and learning are not necessarily related to each other. Chaotic sequences can be learned but not predicted while quasiperiodic sequences can be well predicted but not learned.

Algorithms↗

PubMed2002

Low autocorrelated multiphase sequences.

The interplay between the ground-state energy of the generalized Bernasconi model to multiphase, and the minimal value of the maximal autocorrelation function, C(max)=max(K)/C(K)/, K=1,...,N-1, is examined analytically in the thermodynamic limit where the main results are (a) For the binary case, the minimal value of C(max) over all sequences of length N, minC(max), is 0.435sqrt[N], significantly smaller than the typical value for random sequences O(sqrt[log N]sqrt[N]). (b) A new method to approximate F(max) is obtained using the observation of data collapse. (c) minC(max) is obtained in an energy which is about 30% above the ground-state energy of the generalized Bernasconi model, independent of the number of phases m. (d) For a given m, minC(max) proportional sqrt[N/m] indicating that for m=N, minC(max)=1, i.e., a generalized Barker code exists. The analytical results are confirmed by simulations.

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