Efficient classification of chaotic signals with application to secure communications

This paper presents an exhaustive study on the classification capabilities of an efficient algorithm, which is able to accurately classify non-deterministic signals generated by chaotic dynamical systems, without estimating their probability density function (pdf). Experimental results were compared to other existing techniques such as Hidden Markov Model (HMM), Vector Quantization (VQ), and Dynamic Time Warping (DTW). Classification performance is higher than current best practices for chaotic signals. A better noise rejection was also achieved, and a reduction of two orders of magnitude in training-times compared with HMM was obtained, thus making the proposed methodology one of the current best practices in this field. As an application example, the recognition of encrypted chaotic-signals in a secure-communication context, is reported and discussed.