A New Blowfish Using A Neural Network
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Abstract
The Blowfish algorithm is one of the most popular, but it requires significant computational power with many details that make them prey to many attackers. In this paper, the original Blowfish algorithm was optimized by reducing the size of the "s-box" and then designing and implementing it on a neural network (NN). The input to the neural network is text either the(plaintext or ciphertext) and the output obtained from the network is the equivalent text and the key used in both encryption and decryption are the initial weights of the neural network that are trained using the backpropagation network. The proposed neural network (NN) model was designed and simulated by a computer. Simulation results demonstrate reduced complexity of the algorithm, implementation speed, and convergence of results achieved through the NN-based optimized Blowfish algorithm encryption system with optimized Blowfish results.
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