Secure key-exchange protocol with an absence of injective functions

Concurring of Neural Machines for Robust Session Key Generation and Validation in Telecare Health System During COVID-19 Pandemic

In this technique, it has been proposed to agree the session keys that have been generated through dual artificial neural networks based on the Telecare Health COVID-19 domain. Electronic health enables secure and protected communication between the patients and physicians, especially during this COVID-19 pandemic. Telecare was the main component which served the remote and non-invasive patients in the crisis period of COVID-19. Neural cryptographic engineering support in terms of data security and privacy is the main theme for Tree Parity Machine (TPM) synchronization in this paper. The session key has been generated on different key lengths and key validation done on the proposed set of robust session keys. A neural TPM network receives a vector designed through same random seed and producing a single output bit. Duo neural TPM networks' intermediate keys would be partially shared between the patient and doctor for the purpose neural synchronization. Higher magnitude of co-existence has been observed at the duo neural networks at the Telecare Health Systems in COVID-19. This proposed technique has been highly protective against several data attacks in the public networks. Partial transmission of the session key disables the intruders to guess the exact pattern, and highly randomized through different tests. The average p-values of different session key lengths of 40 bits, 60 bits, 160 bits, and 256 bits were observed to be 221.9, 259.3, 242, and 262.8 (taken under multiplicative of 1000) respectively.

State-of-the-art session key generation on priority-based adaptive neural machine (PANM) in telemedicine

Telemedicine is one of the safest methods to provide healthcare facilities to the remote patients with the help of digitization. In this paper, state-of-the-art session key has been proposed based on the priority oriented neural machines followed by its validation. State-of-the-art technique can be mentioned as newer scientific method. Soft computing has been extensively used and modified here under the ANN domain. Telemedicine facilitates secure data communication between the patients and the doctors regarding their treatments. The best fitted hidden neuron can contribute only in the formation of the neural output. Minimum correlation was taken into consideration under this study. Hebbian learning rule was applied on both the patient’s neural machine and the doctor’s neural machine. Lesser iterations were needed in the patient’s machine and the doctor’s machine for the synchronization. Thus, the key generation time has been shortened here which were 4.011 ms, 4.324 ms, 5.338 ms, 5.691 ms, and 6.105 ms for 56 bits, 128 bits, 256 bits, 512 bits, and 1024 bits of state-of-the-art session keys, respectively. Statistically, different key sizes of the state-of-the-art session keys were tested and accepted. Derived value-based function had yielded successful outcomes too. Partial validations with different mathematical hardness had been imposed here too. Thus, the proposed technique is suitable for the session key generation and authentication in the telemedicine in order to preserve the patients’ data privacy. This proposed method has been highly protective against numerous data attacks inside the public networks. Partial transmission of the state-of-the-art session key disables the intruders to decode the same bit patterns of the proposed set of keys.

Synchronization of Tree Parity Machines Using Nonbinary Input Vectors

Neural cryptography is the application of artificial neural networks (ANNs) in the subject of cryptography. The functionality of this solution is based on a tree parity machine (TPM). It uses ANNs to perform secure key exchange between network entities. This brief proposes improvements to the synchronization of two TPMs. The improvement is based on learning ANN using input vectors that have a wider range of values than binary ones. As a result, the duration of the synchronization process is reduced. Therefore, TPMs achieve common weights in a shorter time due to the reduction of necessary bit exchanges. This approach improves the security of neural cryptography.

Successful attack on permutation-parity-machine-based neural cryptography

An algorithm is presented which implements a probabilistic attack on the key-exchange protocol based on permutation parity machines. Instead of imitating the synchronization of the communicating partners, the strategy consists of a Monte Carlo method to sample the space of possible weights during inner rounds and an analytic approach to convey the extracted information from one outer round to the next one. The results show that the protocol under attack fails to synchronize faster than an eavesdropper using this algorithm.

Permutation parity machines for neural cryptography

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Dynamics of neural cryptography

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.

Genetic attack on neural cryptography

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.

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.

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.

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.