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首页> 外文期刊>Information Sciences: An International Journal >Enhanced digital chaotic maps based on bit reversal with applications in random bit generators
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Enhanced digital chaotic maps based on bit reversal with applications in random bit generators

机译:基于随机比特发生器中的应用程序的增强数字混沌映射

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摘要

Digital chaotic maps are becoming increasingly popular in the area of cryptography due to commonalities but have drawbacks which adversely effect security strength. Thus, enhancing digital chaotic maps in terms of their chaoticity and statistical properties contributes towards the improvement of chaos-based cryptography. This paper proposes a bit reversal approach to address these issues. The proposed method modifies chaotic state values (represented as fixed point numbers) by reversing the order of their fractional bits. Experimental verification indicates that chaotic maps modified by the proposed approach depict better chaotic performance, have higher complexity and larger chaotic parameter range. These results exceed those of existing digital chaotic maps and other chaotification methods. The simplicity of the proposed bit reversal approach and the use of fixed point representation makes it easy to implement on any computing platform. This approach is also highly flexible as it does not require any external inputs, making it a universal method for enhancing any digital chaotic map. As a proof-of-concept, a pseudorandom bit generator (PRBG) was designed based on cascading chaotic maps modified by the proposed method. Simulation and security analysis indicate that the proposed PRBG is statistically random, has a uniform data distribution and high key sensitivity. (C) 2019 Elsevier Inc. All rights reserved.
机译:由于共性,数字混沌地图在加密地区越来越受欢迎,但具有不利影响安全实力的缺点。因此,在复杂性和统计特性方面增强数字混沌图有助于改善基于混沌的密码学。本文提出了一种解决这些问题的有点逆转方法。所提出的方法通过反转其分数位的顺序修改混沌状态值(表示为固定点数)。实验验证表明,通过所提出的方法修改的混沌映射描绘了更好的混沌性能,具有更高的复杂性和更大的混沌参数范围。这些结果超过了现有的数字混沌映射和其他混乱方法。所提出的位反转方法的简单性和定点表示的使用使得在任何计算平台上都可以实现。这种方法也非常灵活,因为它不需要任何外部输入,使其成为增强任何数字混沌映射的通用方法。作为概念验证,基于由所提出的方法改性的级联混沌图设计了一种伪随机比特发生器(PRBG)。仿真和安全分析表明,所提出的PRBG在统计上随机,具有统一的数据分布和高密钥灵敏度。 (c)2019 Elsevier Inc.保留所有权利。

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