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.
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Neumann, D., Eckmiller, R., Baruth, O. (2006). Development of a Neural Net-Based, Personalized Secure Communication Link. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_36
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DOI: https://doi.org/10.1007/11840817_36
Publisher Name: Springer, Berlin, Heidelberg
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