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For conventional cryptographic systems, we can improve the security of the protocol by increasing of the key length. In the case of neural cryptography, we improve it by increasing of the synaptic depth L of the neural networks. Changing this parameter increases the cost of a successful attack exponentially, while the effort for the users grows polynomially. Therefore, breaking the security of neural key exchange belongs to the complexity class NP.
Alexander Klimov, Anton Mityaguine, and Adi Shamir say that the original neural synchronization scheme can be broken by at least three different attacks—geometric, probabilistic analysis, and using genetic algorithms. Even though this particular implementation is insecure, the ideas behind chaotic synchronization could potentially lead to a secure implementation.<ref name="Klimov">{{cite conference |last1=Klimov |first1=Alexander |last2=Mityagin |first2=Anton |last3=Shamir |first3=Adi |date=2002 |title=Analysis of Neural Cryptography |url=https://iacr.org/archive/asiacrypt2002/25010286/25010286.pdf |book-title=Advances in Cryptology |conference=ASIACRYPT 2002 |series=[[Lecture Notes in Computer Science|LNCS]] |volume=2501 |pages=288–298 |issn=0302-9743 |doi=10.1007/3-540-36178-2_18 |accessdate=2017-11-15}}</ref>
=== Permutation parity machine ===
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