Classification of chaotic codes using fuzzy clustering techniques and higher-order statistics features

In this paper, efficient techniques for the classification of chaotic codes are presented. Four different clustering techniques, namely, k-mean clustering, hierarchical clustering, fuzzy c mean clustering, and subtractive clustering are used for classification. Higher order statistics features obtained from some different types of wavelet transform are utilized. The codes to be classified are assumed to be generated by two different methods. The first method is generating different chaotic codes using different chaotic maps with the same initial values. Two types of chaotic maps are considered, namely the logistic map and bended-up-down map. The second method of code generation is to use the same chaotic map with different initial values.