Neural Cryptography Research Articles

WITHDRAWN: Hybrid neural synchronization blowfish algorithm for secret key exchange over public channels

A secured Exchange of information between authorized or dedicated users over the public channels without revealing information to anyone of unauthorized users is the main purpose of a cryptographic system. Since a large computation power is required in cryptographic systems and is involves with complex process, commonly shared secret keys are generated by using the neural synchronization based algorithms which in turn use a large number of learning rules for the exchanging of these secret keys on public channels. In this paper, the secret key is formed as input bits of a neural network on a public channel using a hybrid Blowfish (BF) algorithm for the securing of the files. The information files or messages sent through a public channel can be encrypted and decrypted by using this generated secret key. In this, first he two communication networks in this neural cryptography system produce mutual output bits by receiving the identical input vectors and are trained on their mutual output bits. Identical time independent vectors are calculated to synchronize these communication networks upto a state. Then the generated common secret key can be exchanged on public channels. This secret key can be utilized for the implementation of any encryption and decryption algorithms. According to the throughput and runtimes the proposed algorithm is analyzed by using the medical report files.

Neural Cryptography Based on Complex-Valued Neural Network.

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.

Design of group key agreement protocol using neural key synchronization

Security is considered as an important anxiety for applications which involve communication (more than two) over public networks. Group key management is one of the essential building blocks in securing group communication. Most of the researches in group key management are based on the general idea of the original Diffie–Hellman (DH) key agreement protocol. The major drawback of generalized DH for multi-party is the fact that there are expensive exponential operations to be carried out. The factors that influence the capability of a Group Key agreement (GKA) protocol are communication rounds and the computational cost. In this paper, the concept of neural cryptography, based on the mutual learning of Tree Parity Machine (TPM), is extended to operations to be carried out. The factors that influence the capability of a Group Key agreement (GKA) protocol are communication rounds and the computational cost. In this paper, the concept of neural cryptography, based on the mutual learning of Tree Parity Machine (TPM), is extended to obtain a group key. The group key has been generated for an interactive conference key agreement system, where the system authenticates the users and allows them to compute their own session key, and also it has been recognized that the neural network based GKA protocols achieve key freshness and key secrecy. The quantity demand and investment in research and development, while the other model focuses on a more realistic relationship between the quantity demand and the price.

Single secret image sharing scheme using neural cryptography

The goal of the secret sharing is to share a secret information without any leakage to others. In this paper, we proposed a secure mechanism of sharing secret shares of an image between two parties generated via Shamir’s scheme with the help of neural cryptography. Neural cryptography is a new source for public key cryptography schemes which are not based on number theory, and have less computation time and memory complexities. Neural cryptography can be used to generate a common secret key between two parties. Keeping all these in mind, our main focus is to share secret information over a public channel with less computation power. In the case of neural cryptography, both the parties receive an identical input vector, generate an output bit and are trained based on the output bit. The dynamics of the two networks and their weight vectors is found to exhibit a synchronization state with identical weights. These identical weights are acts as a common key between two parties. The proposed scheme is secure and does not reveal secret information.

Black Box Attack of Simulation Secret Key using Neural-Identifier

The issue of the cryptanalysis is defined as the unknown issue or problem related to the identification of the system where the major goal of the cryptanalysis is for the design of the system for various steps involved. Neural networks will be ideal tool for Black-Box system identification. The black-Box attacks against secret key cryptosystems (stream cipher system) would be presented by considering a Black-Box Neuro-Identifier model to retain two different objectives: first is for finding out the key from the provided plaintext-cipher to pair, while the second objective is to emulator construction a neuro-model for the target cipher system. There are many researches going on considering the various models of encryption where ANN is being used as single layered or multi layered perceptron. The above defined cryptographic techniques are sometimes also termed as the Neural Cryptography. As the ANN model relies on the feedforward working criteria means it can be used for the generation of some effective and efficient encryption methodologies. Cryptanalysis is considered as significant footstep for evaluating and checking quality of any cryptosystem. A portion of these cryptosystem guarantees secrecy and security of huge data trade from source to goal utilizing symmetric key cryptography. The cryptanalyst researches the quality and distinguishes the shortcoming of the key just as enciphering calculation. With the expansion in key size, the time and exertion required anticipating the right key increments. These systems for cryptanalysis are changing radically to decrease cryptographic multifaceted nature. In this paper a point by point study has been directed. Much cryptography strategies are accessible which depend on number hypothesis however it has the hindrance of necessity a substantial computational power, unpredictability and time utilization. To defeat these disadvantages, artificial neural networks (ANNs) have been connected to take care of numerous issues. The ANNs have numerous qualities, for example, learning, speculation, less information necessity, quick calculation, simplicity of usage, and programming and equipment accessibility, which make it exceptionally alluring for some applications. This paper gives a cutting-edge survey on the utilization of counterfeit neural systems in cryptography and concentrates their execution on estimation issues identified with cryptography.

Error correction in quantum cryptography based on artificial neural networks

Intensive work on quantum computing has increased interest in quantum cryptography in recent years. Although this technique is characterized by a very high level of security, there are still challenges that limit the widespread use of quantum key distribution. One of the most important problems remains secure and effective mechanisms for the key distillation process. This article presents a new idea for a key reconciliation method in quantum cryptography. This proposal assumes the use of mutual synchronization of artificial neural networks to correct errors occurring during transmission in the quantum channel. Users can build neural networks based on their own string of bits. The typical value of the quantum bit error rate does not exceed a few percent; therefore, the strings are similar and also users’ neural networks are very similar at the beginning of the learning process. It has been shown that the synchronization process in the new solution is much faster than in the analogous scenario used in neural cryptography. This feature significantly increases the level of security because a potential eavesdropper cannot effectively synchronize their own artificial neural networks in order to obtain information about the key. Therefore, the key reconciliation based on the new idea can be a secure and efficient solution.

On the Development of an Optimal Structure of Tree Parity Machine for the Establishment of a Cryptographic Key

When establishing a cryptographic key between two users, the asymmetric cryptography scheme is generally used to send it through an insecure channel. However, given that the algorithms that use this scheme, such as RSA, have already been compromised, it is imperative to research for new methods of establishing a cryptographic key that provide security when they are sent. To solve this problem, a new branch known as neural cryptography was born, using a modified artificial neural network called Tree Parity Machine or TPM. Its purpose is to establish a private key through an insecure channel. This article proposes the analysis of an optimal structure of a TPM network that allows generating and establishing a private cryptographic key of 512-bit length between two authorized parties. To achieve this, the combinations that make possible to generate a key of that length were determined. In more than 15 million simulations that were executed, we measured synchronization times, the number of steps required, and the number of times in which an attacking TPM network manages to imitate the behaviour of the two networks. The simulations resulted in the optimal combination, minimizing the synchronization time and prioritizing security against the attacking network. Finally, the model was validated by applying a heuristic rule.

Secure Data Fusion Analysis on Certificateless Short Signature Scheme Based on Integrated Neural Networks and Elliptic Curve Cryptography

Secure Data Fusion Analysis on Certificateless Short Signature Scheme Based on Integrated Neural Networks and Elliptic Curve Cryptography

Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography.

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.

MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE

In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights.

Neural cryptography

Neural cryptography

Application of Neural Cryptography in Solving Problems of Information Security

The paper is devoted to consideration of the actual problems connected with cryptographic methods of data protection, based on the use of mechanisms of artificial neural networks. Characteristics, as well as various approaches in implementation of cryptographic problems with use of artificial neural networks, are researched. The detailed overview of the artificial neural networks applied as data protection methods is carried out.

Approach to design neural cryptography: A generalized architecture and a heuristic rule

Neural cryptography, a type of public key exchange protocol, is widely considered as an effective method for sharing a common secret key between two neural networks on public channels. How to design neural cryptography remains a great challenge. In this paper, in order to provide an approach to solve this challenge, a generalized network architecture and a significant heuristic rule are designed. The proposed generic framework is named as tree state classification machine (TSCM), which extends and unifies the existing structures, i.e., tree parity machine (TPM) and tree committee machine (TCM). Furthermore, we carefully study and find that the heuristic rule can improve the security of TSCM-based neural cryptography. Therefore, TSCM and the heuristic rule can guide us to designing a great deal of effective neural cryptography candidates, in which it is possible to achieve the more secure instances. Significantly, in the light of TSCM and the heuristic rule, we further expound that our designed neural cryptography outperforms TPM (the most secure model at present) on security. Finally, a series of numerical simulation experiments are provided to verify validity and applicability of our results.

Two-layer tree-connected feed-forward neural network model for neural cryptography

Neural synchronization by means of mutual learning provides an avenue to design public key exchange protocols, bringing about what is known as neural cryptography. Two identically structured neural networks learn from each other and reach full synchronization eventually. The full synchronization enables two networks to have the same weight, which can be used as a secret key for many subsequent cryptographic purposes. It is striking to observe that after the first decade of neural cryptography, the tree parity machine (TPM) network with hidden unit $K=3$ appears to be the sole network that is suitable for a neural protocol. No convincingly secure neural protocol is well designed by using other network structures despite considerable research efforts. With the goal of overcoming the limitations of a suitable network structure, in this paper we develop a two-layer tree-connected feed-forward neural network (TTFNN) model for a neural protocol. The TTFNN model captures the notion that two partners are capable of exchanging a vector with multiple bits in each time step. An in-depth study of the dynamic process of TTFNN-based protocols is then undertaken, based upon which a feasible condition is theoretically obtained to seek applicable protocols. Afterward, according to two analytically derived heuristic rules, a complete methodology for designing feasible TTFNN-based protocols is elaborated. A variety of feasible neural protocols are constructed, which exhibit the effectiveness and benefits of the proposed model. With another look from the perspective of application, TTFNN-based instances, which can outperform the conventional TPM-based protocol with respect to synchronization speed, are also experimentally confirmed.

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.

Design of An Efficient Neural Key Generation

The goal of any cryptographic system is the exchange of information among the intended users without any leakage of information to others who may have unauthorized access to it. A common secret key could be created over a public channel accessible to any opponent. Neural networks can be used to generate common secret key. In case of neural cryptography, both the communicating networks receive an identical input vector, generate an output bit and are trained based on the output bit. The two networks and their weight vectors exhibit a novel phenomenon, where the networks synchronize to a state with identical time-dependent weights. The generated secret key over a public channel is used for encrypting and decrypting the information being sent on the channel.

On the Improvement of Neural Cryptography Using Erroneous Transmitted Information With Error Prediction

Neural cryptography deals with the problem of "key exchange" between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called "Do not Trust My Partner" (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called "Synchronization with Common Secret Feedback" (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.

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.

A NEW SECURITY ON NEURAL CRYPTOGRAPHY WITH QUERIES EMPLOYED TO CRYPTO-COMPRESSION OF MEDICAL IMAGES IN TELEMEDICINE SYSTEM

There is a necessity to protect the confidential medical images data from an unauthorized access when the exchange of medical information is taken place among the patients and doctors. We can generate a common secret key using neural cryptography, which is based on synchronization of Tree Parity Machines (TPMs) by mutual learning. In the proposed TPMs replacing random inputs with queries are considered, which depend on the current state of the neural network. Then, TPMs hidden layer of each output vectors are compared. That is, the output vectors of hidden unit using Hebbian learning rule, leftdynamic hidden unit using Random walk learning rule and right-dynamic hidden unit using Anti-Hebbian learning rule are compared. Among the compared values, one of the best values is received by the output layer. The medical image is encrypted using Rijndael Encryption and compressed using Huffman Coding, in order to fully dissimulate the visual information of the medical image, which is produced as Crypto-Compressed and Encrypted Medical Images (CCEMI). A network with queries generates a secret key, which can be used to encrypt and decrypt of medical images. We have shown that it is more difficult to break a secret key by brute force attack.

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.