Implementation of neural-cryptographic system using fpga

Modern cryptography techniques are virtually unbreakable. as the internet and other forms of electronic communication become more prevalent,electronic security is becoming increasingly important. cryptography is used to protect email messages,credit card information,and corporate data. the design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. the chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. the central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. pseudo random number generators (prng) have been widely used to construct this key sequence. the pseudo random number generator was designed using the artificial neural networks (ann). the artificial neural networks (ann) providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. the learning algorithm of this neural network is backpropagation learning algorithm. the learning process was done by software program in matlab (software implementation) to get the efficient weights. then,the learned neural network was implemented using field programmable gate array (fpga). © school of engineering,taylor's university.