The proposed method for implementing cryptographic traffic detector systems and evasive tools in this article is a method based on deep learning, which due to the importance of optimal and automatic extraction of features from the input data set, an automatic encoder network has been used in the feature extraction phase. Then, the output of the middle hidden layer of this network is applied to a deep convolutional neural network. Deep convolutional neural networks can play an effective role in improving the performance of the system by taking into account the spatial connections between features. Finally, the output of deep convolutional neural network is also applied to two fully connected layers in order to perform the classification process. So that the number of neurons in the second fully connected layer will be equal to the expected number of layers of the system. In the first part of this article, the specifications of the proposed systems for the implementation of cryptographic traffic detectors and evasion tools will be mentioned in terms of operational architecture and features. Then, the characteristics of the networks used in the implementation of the system, and the method of their integration to form the final identifier system are briefly stated. After that, the training stage of the system and its implementation method will be introduced and at the end, how to adjust the parameters of the model and the mechanisms used to improve the overall performance of the system and the results of its performance evaluation will be presented.