A Backpropagation Neural Network for Computer Network Security

In this paper, an efficient and scalable technique for computer network security is presented. On one hand, the decryption scheme and the public key creation used in this work are based on a multi-layer neural network that is trained by backpropagation learning algorithm. On the other hand, the encryption scheme and the private key creation process are based on Boolean algebra. This is a new potential source for public key cryptographic schemes which are not based on number theoretic functions and have small time and memory complexities. This paper along with test results show that the possibility of guessing keys is extremely weaker than using the Data Encryption Standard method (DES), which is a widely-used method of data encryption. The presented results are obtained through the use of MATLAB 6.5.1 software.