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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 468567, 9 pages
Research Article

On the Cryptanalysis of Two Cryptographic Algorithms That Utilize Chaotic Neural Networks

1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON, Canada K1S 5B6

Received 14 October 2014; Revised 30 January 2015; Accepted 3 February 2015

Academic Editor: Joao B. R. Do Val

Copyright © 2015 Ke Qin and B. John Oommen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper deals with the security and efficiency issues of two cipher algorithms which utilize the principles of Chaotic Neural Networks (CNNs). The two algorithms that we consider are (1) the CNN-Hash, which is a one-way hash function based on the Piece-Wise Linear Chaotic Map (PWLCM) and the One-Way Coupled Map Lattice (OCML), and (2) the Delayed CNN-Based Encryption (DCBE), which is an encryption algorithm based on the delayed CNN. Although both of these cipher algorithms have their own salient characteristics, our analysis shows that, unfortunately, the CNN-Hash is not secure because it is neither Second-Preimage resistant nor collision resistant. Indeed, one can find a collision with relative ease, demonstrating that its potential as a hash function is flawed. Similarly, we show that the DCBE is also not secure since it is not capable of resisting known plaintext, chosen plaintext, and chosen ciphertext attacks. Furthermore, unfortunately, both schemes are not efficient either, because of the large number of iteration steps involved in their respective implementations.