TY  - JOUR
T1  - Machine Learning Based Key Generating for Cryptography
AU - Atee, Hayfaa A. AU - Ahmad, Robiah AU - Noor, Norliza Mohd AU - Ilijan, Abidulkarim K. 
JO  - Journal of Engineering and Applied Sciences
VL  - 11
IS  - 8
SP  - 1829
EP  - 1834
PY  - 2016
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2016.1829.1834
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2016.1829.1834
KW  - ELM
KW  -ANN
KW  -sub-key generation
KW  -cryptographic systems
KW  -PRNG
AB  - An efficient key generation technique is demanding for a greatly secured cryptosystem. Traditional key generation techniques are very systematic which makes it subject to attack easily. The inclusion of Artificial Neural Network (ANN) algorithm in the cryptosystem is found to enhance the cryptographic performance in terms security and robustness to attack. Based on Extreme Learning Machine (ELM) for one hidden layer NN, we propose a sub-key generation approach for achieving a good cryptosystem. To initialize the input-hidden layer weights and data in each round, the initial key has been designed to include the ANN topology, activation function and seeds for Pseudo Random Number Generator (PRNG). The sub-key in each round is created using the output layer weights. Evaluation measures of the developed approach demonstrated complete sensitivity and predictability. Furthermore, the achieved remarkable reduction in the risks of breaking the symmetric key algorithm is attributed to the generation of independent sub-key in each round. Our secured sub-key generation approach may contribute towards the development of a secured cryptographic system.
ER  -