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1. A Novel Stream Encryption Scheme with Avalanche Effect | |||
MIN Le-Quan,CHEN Guan-Rong | |||
Information Science and System Science 21 January 2013 | |||
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Abstract:The strict key avalanche effect requires that whenever any bit of the key is changed , theciphertext should have a change with the probability ofone-half. This paper proposes a novel stream encryption scheme withavalanche effect. Using this scheme and an ideal pseudorandom numbergenerator (PRNG) to generate $d$-bit segment binary key streams, onecan encrypt a plaintext such that by using any key stream generatedfrom a different seed to decrypt the ciphertext, the decryptedplaintext will become an avalanche-like text which has 2d- 1 consecutive one's with a high probability. A correspondingavalanche-type encryption theorem is established.As an application, two chaotic PRNGs are designed based on adiscrete Chua circuit and a discrete Chen equation. FIPS 140-2 testson the two PRNGs show that the generated key streams have soundrandomness. SESAE is then used with key streams generated by the twoPRNGs, RC4 PRNG and Matlab PRNG, to encrypt and decrypt two RGBimages, respectively. Results show that the encrypted images haveavalanche effects similar to the ideal d-bit segment binary PRNG,which is not achieved by the images encrypted using the Matlab PRNG. | |||
TO cite this article:MIN Le-Quan,CHEN Guan-Rong. A Novel Stream Encryption Scheme with Avalanche Effect[OL].[21 January 2013] http://en.paper.edu.cn/en_releasepaper/content/4514960 |
2. Optimization-based Structure Identification of Complex Dynamical Networks | |||
He Tao ,Lv Xiliang ,Wu Xiaoqun,Lu Junan ,Zheng Weixing | |||
Information Science and System Science 08 April 2012 | |||
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Abstract:The topological structure of a complex network plays a pivotal part in itsproperties, dynamics and control. Thus, understanding and modeling thestructure of a complex network will lead to a better knowledge of itsevolutionary mechanisms and to a better cottoning on its dynamical andfunctional behaviors. However, in many practical situations, thetopological structure of a complex network is usually unknown or uncertain.Thus, exploring the underlying topological structure of a complex networkis of great value. In recent years, there has been a growing interest instructure identification of complex dynamical networks. As a result, variousmethods for identifying the network structure have been proposed. However,in most of the previous work, few of them were discussed in the perspectiveof optimization. In this paper, an optimization algorithm based on theprojected conjugate gradient method is proposed to identify a networkstructure. It is straightforward and applicable to networks with or withoutobservation noise. Furthermore, the proposed algorithm is also applicable tocomplex networks with partially observed component variables for eachmultidimensional node. Numerical experiments are conducted to illustrate thegood performance and universality of the new algorithm. | |||
TO cite this article:He Tao ,Lv Xiliang ,Wu Xiaoqun, et al. Optimization-based Structure Identification of Complex Dynamical Networks[OL].[ 8 April 2012] http://en.paper.edu.cn/en_releasepaper/content/4474570 |
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