Binary Protocol Classification based on Information Entropy and Convolution Neural Network

Abstract

In order to solve the problem that there are many kinds of unknown binary protocols on the network, which are not easy to manage, In order to ensure the safe and orderly operation of the network, it is necessary to classify the traffic in the network. In this paper, a binary protocol classification method based on a class of classification and one-dimensional CNN (convolution neural network) is proposed, which is trained by the tags of the protocol data obtained by clustering. The binary protocol message is directly used as the input of one-dimensional convolution neural network, and the classification model is trained to realize the automatic classification function of the protocol. A binary protocol classifier is constructed, which can automatically learn the nonlinear relationship between the original input and the expected output. As far as we know, this is the first time that Information Entropy and CNN networks have been applied to the field of binary protocol classification. The experimental results show that the recognition rate of the protocol is up to 98%, and the classification time is better than that of the clustering method. The results show that the method is effective.