Abstract: Neural Cryptography represents an innovative intersection of cryptography and neural networks, particularly in the realms of cryptanalysis and encryption. This paper aims to showcase the capacity of Neural Networks to perform symmetric encryption even in adversarial scenarios, drawing inspiration from previous works in this domain. The fundamental goal of cryptography is to create a cypher that is resistant to deciphering without the corresponding key, thus safeguarding the plaintext. Messages are encrypted using robust cryptography, rendering brute-force attacks against the algorithm or key nearly insurmountable. Robust cryptography achieves this by utilizing exceptionally lengthy encryption keys and encryption algorithms resistant to various forms of attacks. The integration of neural networks marks the next evolutionary phase in the evolution of secure encryption. This paper delves into the practical application of neural networks in cryptography, exploring the development of neural networks tailored for cryptographic purposes.

Keywords: Cryptography key, encryption system, encryption algorithm, artificial neural network, chaos maps, logistic encryption.

Asst. Prof. Jyotsna Nanajkar, Sakshi Shinde, Piyush Mishra, Sanjeev Pandey, Ankit Tiwari, "Neural Network based Message Concealment Scheme", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 1, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13120.

PDF | DOI: 10.17148/IJARCCE.2024.13120

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