Development of a Neural Net-Based, Personalized Secure Communication Link

  • Dirk Neumann
  • Rolf Eckmiller
  • Oliver Baruth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)


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.


Input Pattern Secure Communication Learning Module Modulation Vector Biometric Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dirk Neumann
    • 1
  • Rolf Eckmiller
    • 1
  • Oliver Baruth
    • 1
  1. 1.Department of Computer Science, Division of Neural ComputationUniversity of BonnBonnGermany

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