Data protection based neural cryptography and deoxyribonucleic acid - Sahar Adill Kadum; Ali Yakoob Al-Sultan; Najlaa Adnan Hadie (Institute of Advanced Engineering and Science, 2022)
The need to a robust and effective methods for secure data transferring makes the more credible. Two disciplines for data encryption presented in this paper: machine learning and deoxyribonucleic acid (DNA) to achieve the above goal and following common goals: prevent unauthorized access and eavesdropper. They used as powerful tool in cryptography. This paper grounded first on a two modified Hebbian neural network (MHNN) as a machine learning tool for message encryption in an unsupervised method. These two modified Hebbian neural nets classified as a: learning neural net (LNN) for generating optimal key ciphering and ciphering neural net CNN) for coding the plaintext using the LNN keys. The second granulation using DNA nucleated to increase data confusion and compression. Exploiting the DNA computing operations to upgrade data transmission security over the open nets. The results approved that the method is effective in protect the transferring data in a secure manner in less time