A digital image encryption scheme based on the hybrid of cellular neural network and logistic map
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
An anomaly intrusion detection approach using cellular neural networks
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Traceability of executable codes using neural networks
ISC'10 Proceedings of the 13th international conference on Information security
Research on a novel image encryption scheme based on the hybrid of chaotic maps
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
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Using Chaotic characteristics of dynamic system is a promising direction to design cryptosystems that play a pivotal role in a very important engineering application of cognitive informatics, i.e., information assurance and security. However, encryption algorithms based on the lowdimensional chaotic maps face a potential risk of the keystream being reconstructed via return map technique or neural network method. In this paper, we propose a new digital image encryption algorithm that employs a hyper-chaotic cellular neural network. To substantiate its security characteristics, we conduct the following security analyses of the proposed algorithm: key space analysis, sensitivity analysis, information entropy analysis and correlation coefficients analysis of adjacent pixels. The results demonstrate that the proposed encryption algorithm has desirable security properties and can be deployed as a cornerstone in a sound security cryptosystem. The comparison of the proposed algorithm with five other chaos-based image encryption algorithms indicates that our algorithm has a better security performance.