3D CUBE Algorithm for the Key Generation Method: Applying Deep Neural Network Learning-Based

• Main Authors: Jungha Jin, Keecheon Kim
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Artificial neural networks; encryption; information security; public key; random number generation
• Online Access: https://ieeexplore.ieee.org/document/8998197/

Current encryption systems are run with a hybrid mode in which symmetric and asymmetric key methods are mixed. This hybrid mode is devised to employ the fast processing speed of the symmetric key while circumventing the difficulty of providing services due to computational complexity of the asymmetric key method by limiting its use to secured key exchange. To implement a secured symmetric key-based cryptographic system to circumvent the crack problem due to the emergence of quantum computers in asymmetric key exchanges, the problem of sharing a key transferred in the most secure manner should be solved while symmetric keys in use are kept up-to-date. This paper proposes a three-dimensional (3D) cube algorithm that can be used by creating the up-to-date key in a symmetric key encryption system to provide security resistance in a quantum computer environment. More specifically, it presents a solution to the secured key sharing method that can minimize the damage due to the pre-shared key (PSK) leakage. This is done by using the method of inducing the symmetric key creation based on deep neural network learning without sharing the PSK between systems to securely maintain the key while creating and using the key that is variably used through the symmetric key encryption system of the 3D cube algorithm. Thus, the proposed algorithm secures confidentiality and integrity of data transferred over a network because information that can be obtained by malicious attackers is small (because the symmetric key used in encryption and decryption is induced without exchanging the PSK with the application of deep neural network learning).