Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Random Sequence Generation Algorithm for Multi-chaotic Systems

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  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_14,
        author={Xiaodi Chen and Hong Wu},
        title={Random Sequence Generation Algorithm for Multi-chaotic Systems},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Multi-chaos system Logistic map Cubic map Chaotic sequence Randomness},
        doi={10.1007/978-3-030-00557-3_14}
    }
    
  • Xiaodi Chen
    Hong Wu
    Year: 2018
    Random Sequence Generation Algorithm for Multi-chaotic Systems
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_14
Xiaodi Chen1, Hong Wu1,*
  • 1: Heilongjiang University
*Contact email: 2002060@hlju.edu.cn

Abstract

The characteristics of chaotic signals, such as pseudorandom and non long term predictability, make it suitable for application to information encryption, digital watermarking and so on. Nevertheless, since the chaotic system is often characterized by its characteristics, attackers can take advantage of these known features to reduce the difficulty of attacks. In contrast, the characteristics of multi-chaotic systems are not uniform, and the complexity of generating sequences is higher than that of single-chaos systems. Hence, the multi-chaotic system increases the security of the sequence to some extent. Therefore, we design a random sequence generation algorithm consisting of multiple chaotic systems that is a chaotic sequence generation algorithm combining Logistic map and Cubic map. And we analyze the sequence of new generation whose the performance, so we can conclude that the new algorithm has better randomness.