Performance and Statistical Analysis of Chaotic Random Bit Generator

S. M. A. Maraikkayar, K. Rajakumar, R. Tamilselvi, M. Beham, A. Afroze
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Abstract

Medical Information transmitted through the internet and secured against any attacks is an international challenging fear. In the present scenario, a fabulous relation emerges between chaos and cryptography. Various features of chaotic systems such as initial state sensitivity, ergodicity, mixing properties and complexity in the structure produce deterministic pseudo randomness in the input data. Chaotic Random Bit Generator (CRBG) makes the bit sequence unpredictable by an intruder, in the field of medical research. In current years, mixture of chaos-based cryptosystems have been projected. To be used in medical field, a CRBG may require in meeting stronger desires than for any other applications. Motivated by all these issues, a novel chaotic random bit generator is proposed based on two different chaotic based logistic maps in parallel and with preliminary self-determining initial conditions. The random bit sequence which is chaotic in character is created by predicting the outputs of both the chaotic logistic maps. Also it is projected to put forward dissimilar tests by stressing some of its alluring arithmetic features, which make it an ideal preference for the expected random bit generation. Lastly, the results of all the statistical tests generated bit sequences, is tested under all the most powerful NIST suit tests for the prediction of randomness: The tests validate the exact expected uniqueness expected of real random sequences.
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混沌随机比特发生器的性能与统计分析
通过互联网传输医疗信息并确保其不受任何攻击是一项国际挑战。在当前的场景中,混沌和密码学之间出现了一种奇妙的关系。混沌系统的初始状态敏感性、遍历性、混合性和结构复杂性等特性导致输入数据具有确定性的伪随机性。在医学研究领域,混沌随机比特发生器(CRBG)使比特序列无法被入侵者预测。近年来,人们预测了基于混沌的混合密码系统。为了在医学领域使用,CRBG可能需要满足比任何其他应用更强烈的需求。在此基础上,提出了一种基于两种不同混沌逻辑映射的混沌随机比特发生器,该混沌逻辑映射具有自确定初始条件。通过预测这两个混沌逻辑映射的输出,产生具有混沌特征的随机位序列。此外,通过强调其一些诱人的算术特征,提出了不同的测试,使其成为期望随机比特生成的理想选择。最后,在所有最强大的NIST随机预测套装测试下,对生成的比特序列的统计测试结果进行了测试:测试验证了真实随机序列的期望唯一性。
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