An adaptive neural network guided secret key based encryption through recursive positional modulo-2 substitution for online wireless communication (ANNRPMS)

In this paper, a neural network guided secret key based technique for encryption (ANNRPMS) has been proposed using recursive positional modulo-2 substitution 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. The dynamics of the two networks and their weight vectors are found to a novel experience, where the demonstrate networks synchronize to an identical time dependent weight vectors. This observable fact has been used to form a secured variable length secret-key using a public channel. The length of the key depends on the number of input and output neurons. The original plain text is encrypted using recursive positional modulo-2 substitution technique. The secret key also fabricated through encryption process in a 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 recursive positional modulo-2 substitution encrypted cipher text and secret key for decoding. A session key based transmission has also been proposed using 161-bit key format of 14 different segments. 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.