Non-linear Keystream Generation for Encryption

C.-K. Chan and C.M. Ng (PRC)


Hopfield neural network, encryption, keystream.


Sensitive information that sends over public channels can be safeguarded by encrypting it. Encryption is the mutation of information into a representation unreadable by anyone without the decryption. In this paper a new encryption system based on the nonlinear property of the Hopfield neural network is proposed. The system is implemented by cascading the Clipped Hopfield Neural Networks in a parallel architecture. Experience results show that the output keystreams have a near optimal linear complexity, which is suitable for cryptographic systems with a high level of security.

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