Genetic Key Guided Neural Deep Learning based Encryption for Online Wireless Communication (GKNDLE)

摘要

In this paper, a neural deep learning guided genetic secret key based technique for encryption (GKNDLE) has been proposed for online transmission of data/information through wireless communication. In this approach both the communicating networks receive an indistinguishable input vector, produce an output bit and are trained based on the output bit. Synchronized weight vectors become the chromosome of the 1st generation Genetic Algorithm based secret key used to encrypt the plain text. Intermediate encrypted stream goes through another triangular cipher process. A secret key is also fabricated in the process of encryption as cascaded manner. This intermediate cipher text is again encrypted to form the final cipher text through chaining and cascaded xoring of identical weight vector with the identical length intermediate cipher text block. If size of the last block of intermediate cipher text is less than the size of the key then this block is kept unchanged. Receiver will use identical weight vector for performing deciphering process for getting the triangular encrypted cipher text and secret key for decoding. Finally GA secret key helps to generate ultimate plain text. A session key based transmission has also been proposed using 161-bit key format of 14 different segments [7,8]. Parametric tests are done and results are compared in terms of Chi-Square test, response time in transmission with some existing classical techniques, which shows comparable results for the proposed system.