Research On Random Sequence Measures And1D Visualization Of Variant System

Cryptography is the core technology for network information security, and random sequences play an important role in the cryptographic technology. Due to the security of the stream cipher depends on the randomness and unpredictability of the key stream, the randomness strength of key stream is an import indicator of stream cipher. The method of generating key stream sequences and randomness analysis are hot topics in network information security, and also, the randomness analysis of random sequence needs more study. Mathematical statistics models are widely used in analysis of pseudorandom sequences. The quantitative index is obtained through the measurement and analysis of statistical characteristics of pseudorandom sequence, which determines the performance of pseudorandom sequence. Quantitative measure can be directly obtained by statistics and measurements for a single sequence. But there are some problems in systematically test multi-segments and complex sequences. It is difficult to visually observe the systematic distribution of sequence from the mass of statistical results.In this thesis, measurement and visualization model of random sequence from the perspective of variant measurement is researched. The core works are focused on the formation and establishment for1D visualization model of random sequences, which based on the variant system. This system includes three modules:variant data generation, measurement and visualization. Firstly, the initial sequence input to the variant data generation module. Sequence pairing and variant conversion is in this step. The output is named variant sequence. Then variant sequence is segmented, and eight measure sequences can be obtained in the probability statistics method of measurement module. Finally, the distribution characteristics of sequence will be displayed through1D characteristics visualization graphics.Stream cipher and DNA sequence are two data test cases for measurement visualization model that established in this thesis. The appropriate parameters are selected, besides, the DNA sequences and generated key stream sequences of algorithm RC4and HC-256are input to the system to measure and visualize. Finally, the distribution characteristics of different sequences are compared in graphs.