Hybrid encryption technique: Integrating the neural network with distortion techniques

ABSTRACT: This paper proposes a hybrid technique for data security. The computational model of the technique is grounded on both the non-linearity of neural network manipulations and the effective distortion operations. To accomplish this, a two-layer feedforward neural network is trained for each plaintext block. The first layer encodes the symbols of the input block, making the resulting ciphertext highly uncorrelated with the input block. The second layer reverses the impact of the first layer by generating weights that are used to restore the original plaintext block from the ciphered one. The distortion stage imposes further confusion on the ciphertext by applying a set of distortion and substitution operations whose functionality is fully controlled by random numbers generated by a key-based r

SUBMITTER: Abu Zitar R 

PROVIDER: S-EPMC9518910 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature