Three-Dimensional Visual Method Of Analysis Model For Multi-valued Random Sequences

Research and application on random se(?)ences has a long history. The concept of a random sequence is essential in probability and statistics theory. With the continuous development of information security and communications technology, the status of random sequences is increasingly important. Research for the random sequence focused on constructing a high randomness and efficient pseudo-random sequence generator. In Security Applications, stream cipher has been widely used in network security protocols to ensure the secure transmission of information. However, the security of stream cipher mechanisms depends on pseudo-random sequence generator. The most important criteria of evaluating the merits of a random sequence generator are statistical randomness. Currently, there are hundreds of methods for testing the randomness of random sequence, but many of these approaches are based on the same principle. This paper proposes a randomness test method that based on variant logic theory. Two processing mechanisms are proposed depend on different types of random sequence including single-valued and multi-valued random sequences. In order to observe the distributions of measure values, a three-dimensional visualization model is used to show the distributions of measure values with different combinations. The paper introduces the research background firstly, and then proposes a three-dimensional visual method based on variant logic theory, which suitable for both single-valued random sequences and multi-valued random sequences. The main modules of this whole system will be described, and the input and output of each module will be described in detail. For single-valued random sequences, two typical pseudo-random generation mechanisms are discussed including Cellular Automata and RC4stream cipher. Under the three-dimensional visualization model of variant logic, a group of pseudo-random sequences generated by different logic functions are compared with a group of pseudo-random sequences generated by RC4algorithm. For multi-valued random sequences, this paper selects stock data and ECG data as input data to measure. Three different sets of stock data are selected to establish three-dimensional visualization analysis model, which based on variant logic measurement to draw the distribution maps, and the results were analyzed to compare with each other. In order to investigate the3D spatial distribution of ECG data, the sample of disease suffering from tachycardia that includes five lead signals are collected. Five lead signals are inputted to this model respectively, as the result,3D spatial distribution maps are analyzed to compare with each other. Different random sequence generators can be distinguished by observing the3D spatial distribution maps which generated by the analysis model of single-valued random sequences generates. And the3D spatial distribution of random sequence generated by RC4stream cipher is great difference compared with which generated by Cellular Automata. This study laid a foundation for testing randomness of stream cipher. As the test result of3D spatial distribution of multi-valued random sequences, there are both similarities and differences between stock data and ECG data. This paper breaks new ground in the study of variant measurement of multi-valued random sequences. In addition, it has some reference value to visual ECG data and stock data.