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Abstract
Commonly, researchers attempt to attack previous cryptography methods allowing access to the key and the end user. Most of the attack methods
used statistical technique or style experience and repetition. In this
research a new method of attack is found using a Neuro-identifier.
Building such Neuro-identifier provides working on behavioral
simulation method and through simulations that we could get a key user
of these roads.
In this Dissertation, the construction of the equivalent Neuro-
Identifier model for cipher systems has been presented and considered as
a new addition in cryptography. The Artificial Neural Networks and
System Identifications have been used to construct the Neuro-Identifier
model which emulates the behavior of cipher systems. The constructed
Neuro-Identifier has been used in two different approaches; the objective
of the first approach is to determine the enciphering key or the plaintext,
and the cryptanalysis objectives are achieved in this approach
(Cryptanalysis) Mode, which assumes no priori knowledge about the
cipher system except its input and output. The aim of the second
approach is to build an equivalent Neuro-Identifier Model for the target
system (Emulation Mode), which is a valuable product in many cases
where the real system is not available. In this Dissertation the Neuro-
Identifier used also for the comparison with the proposed system and
capture the benefits of it.