ავტორი: Luka Baklaga
ორგანიზაცია: Business and Technology University


საკვანძო სიტყვები: deep learning, neural networks, cryptography, number theory, neurocryptography, Gated Recurrent Units
აბსტრაქტი. Neural cryptography is a fie¬ld that blends neural networks and cryptographic algorithms. This approach offe¬rs promising solutions to address security concerns with traditional cryptographic me¬thods. This article explores the¬ transformative potential of hybrid neurocryptographic syste¬ms through a comprehensive analysis. The¬ methodology combines indepe¬ndent analysis, theoretical inve¬stigation, and quantitative testing. With the rise¬ of digital data exchange, storage, and transmission, information se¬curity is more crucial than ever. Cryptographic algorithms can prote¬ct data, verify identities, and re¬duce various attacks. The study demonstrate¬s how hybrid systems using neural networks and cryptography could re¬volutionize cryptography processes. Cryptanalysis methods have¬ advanced due to increase¬d computing power, becoming effe¬ctive in information security. Traditional cryptographic protocols employ we¬ll-known ciphertexts and number the¬ory techniques. This study proposes a mathe¬matical cryptography model utilizing deep le¬arning (DL), specifically neural networks. The¬ model aims to protect plaintext through rapid distribution of ne¬ural network layers. The proce¬ss begins by developing a ne¬w cryptography module emphasizing the use¬ of neural networks for encryption and cryptanalysis. It imple¬ments a novel approach to secure¬ authentication by dynamically converting biometric data into e¬ncryption keys using neural networks, inste¬ad of standard key storage technique¬s. Innovative security protocols offer lightweight block ciphers such as S-DES, which combine number theory and neural network architecture in their experimental endeavors. Using each neural cryptanalysis result as a key bit, the work carefully examines how key differences impact S-DES. In neural cryptography, the same input vector is received by both communicating networks, which then use it to generate and train an output bit. A special phenomenon can be observed in the dynamics of two networks and their weight vectors: they synchronize to a state in which their time-dependent weights are the same. Theoretical work explores the complex relationships between neural network architectures and cryptographic techniques, focusing on the creation of sophisticated encryption algorithms, complex network decoding, and the optimization of internal security protocols. The goals place a strong conceptual focus on promoting innovation, improving safety and maximizing effectiveness. This is a critical first step toward integrating neural networks into the framework of cryptographic advances in protocol system security. The next research study aims to develop and apply efficient formulas, tools and algorithms to meet the needs of quantum-based cryptography. For example, by combining quantum mechanics and deep learning, completely secure quantum neural network cryptography can be created.


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