S. M. A. Maraikkayar, K. Rajakumar, R. Tamilselvi, M. Beham, A. Afroze
{"title":"Performance and Statistical Analysis of Chaotic Random Bit Generator","authors":"S. M. A. Maraikkayar, K. Rajakumar, R. Tamilselvi, M. Beham, A. Afroze","doi":"10.17706/ijcce.2019.8.2.83-92","DOIUrl":null,"url":null,"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.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17706/ijcce.2019.8.2.83-92","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.