The Improvement of Encryption and Decryption on Bi-directional Association Memory based Neural Network
碩士 === 國立臺灣師範大學 === 機電科技研究所 === 96 === Most algorithms developed for encryption and decryption were concentrated on logic analysis. But it is complex for system construction and difficult to apply wide-spread. Recently, even though biomimetic-based architecture of artificial neural network was propo...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/96d34b |
| Summary: | 碩士 === 國立臺灣師範大學 === 機電科技研究所 === 96 === Most algorithms developed for encryption and decryption were concentrated on logic analysis. But it is complex for system construction and difficult to apply wide-spread. Recently, even though biomimetic-based architecture of artificial neural network was proposed to improve reliability and performance of encryption methods such as back-propagation and overstoraged Hopfield Neural Network were developed to fulfill this expectation. But the limitations of encryption capacity, complexity and data completeness after decryption, reliability are still needed to overcome.
This paper proposed a new algorithm to improve reliability and convenience of encryption and decryption with reformed Bi-directional Association Memory (BAM) model to reduce spurious states and data separation caused by former local minima information analysis based on Hebbian learning rule. The space transformation was used to escape crosstalk and noise vector caused by spurious states to keep the completeness of processed information in addition to enhance its security. MATLAB simulation model was used to testify the performance of BAM cryptosystem. The experimental results showed that the security of this proposed system has been improved by Shannon’s perfect secrecy conception.
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