Fault Injection and a Timing Channel on an Analysis Technique
EUROCRYPT '02 Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques: Advances in Cryptology
Tree Parity Machine Rekeying Architectures
IEEE Transactions on Computers
Improved security of neural cryptography using don't-trust-my-partner and error prediction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Research for a dynamic key establishing algorithm suitable for mobile computer
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
IEEE Transactions on Neural Networks
Neuro-Cryptanalysis of DES and Triple-DES
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
An approach for designing neural cryptography
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Journal of Computer Security
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In this paper we analyse the security of a new key exchange protocol proposed in [3], which is based on mutually learning neural networks. This is a new potential source for public key cryptographic schemes which are not based on number theoretic functions, and have small time and memory complexities. In the first part of the paper we analyse the scheme, explain why the two parties converge to a common key, and why an attacker using a similar neural network is unlikely to converge to the same key. However, in the second part of the paper we show that this key exchange protocol can be broken in three different ways, and thus it is completely insecure.