2 research outputs found
Using Echo State Networks for Cryptography
Echo state networks are simple recurrent neural networks that are easy to
implement and train. Despite their simplicity, they show a form of memory and
can predict or regenerate sequences of data. We make use of this property to
realize a novel neural cryptography scheme. The key idea is to assume that
Alice and Bob share a copy of an echo state network. If Alice trains her copy
to memorize a message, she can communicate the trained part of the network to
Bob who plugs it into his copy to regenerate the message. Considering a
byte-level representation of in- and output, the technique applies to arbitrary
types of data (texts, images, audio files, etc.) and practical experiments
reveal it to satisfy the fundamental cryptographic properties of diffusion and
confusion.Comment: 8 pages, ICANN 201
Chosen-Plaintext Cryptanalysis of a Clipped-Neural-Network-Based Chaotic Cipher
In ISNN'04, a novel symmetric cipher was proposed, by combining a chaotic
signal and a clipped neural network (CNN) for encryption. The present paper
analyzes the security of this chaotic cipher against chosen-plaintext attacks,
and points out that this cipher can be broken by a chosen-plaintext attack.
Experimental analyses are given to support the feasibility of the proposed
attack.Comment: LNCS style, 7 pages, 1 figure (6 sub-figures