Complexity analysis of chaotic pseudo-random sequences based on spectral entropy algorithm

Abstract

To analyze the complexity of chaotic pseudo-random sequences accurately, spectral entropy (SE) algorithm is used to analyze chaotic pseudo-random sequences generated by Logistic map, Gaussian map or TD-ERCS system. The SE algorithm has few parameters, and has high robustness with the sequence length N (the only parameter) and the pseudo-random binary number K. Using sliding window method, the evolution features are analyzed, and complexity of discrete chaotic systems with different initial conditions and system parameters are calculated. The results show that SE algorithm is effective for analyzing the complexity of the chaotic pseudo-random sequences, and TD-ERCS is a high complexity system with wide parameter range, and has the best complex performance among the three chaotic systems. The complexity of the same chaotic system with different initial values fluctuates within a small range. It provides a theoretical and experimental basis for the applications of chaotic sequences in the field of information security.