Development of a neural net-based, personalized secure communication link

Authors:
Dirk Neumann;Rolf Eckmiller;Oliver Baruth
Affiliations:
Department of Computer Science, Division of Neural Computation, University of Bonn, Bonn, Germany;Department of Computer Science, Division of Neural Computation, University of Bonn, Bonn, Germany;Department of Computer Science, Division of Neural Computation, University of Bonn, Bonn, Germany
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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Abstract

This paper describes a novel ultra-secure, unidirectional communication channel for use in public communication networks, which is based on a) learning algorithms in combination with neural nets for fabrication of a unique pair of modules for encryption and decryption, and b) in combination with decision trees for the decryption process, c) signal transformation from spatial to temporal patterns by means of ambiguous spatial-temporal filters (ST filters), d) absence of public- or private keys, and e) requirement of biometric data of one of the users for both generation of the pair of hardware/software modules and for the decryption by the receiver. To achieve these features we have implemented an encryption-unit (EU) using ST filters for encryption and a decryption unit (DU) using learning algorithms and decision trees for decryption.