Unveil the Time Delay Signature in Delayed Chaotic Communication System via CNN

With the rapid development and popularization of IoT devices in our daily lives, secure data transmission has become a critical and challenging issue in resource-constrained devices. Simple chaotic circuits can provide highly efficient data encryption and transmission simultaneously. Time delay feedback system is considered as a promising solution to meet the demand for generating highly complex chaotic signal. The time delay is an important parameter that significantly influences the security of such systems. Although there exist many methods that can extract this parameter by adopting the leaked chaotic time series and certain information of the system model, they more or less face some restrictions, especially when the system nonlinearity is quite strong. To break through these limitations, we propose a novel method that can extract the key (i.e., time delay signature, TDS) of time delay chaotic systems by machine learning strategy. The 1D time series transmitted from a time delay chaotic system is embedded in 2D planes to generate training images. A convolutional neural network (CNN) is explored to learn from these images to identify common and different features for TDS extraction. Simulation results verify the feasibility and efficiency of the proposed scheme.