Application Layer Protocol Recognition Based on Residual Network and Recurrent Neural Network

Abstract

Existing protocol recognition methods cannot effectively extract the temporal and spatial characteristics of protocol data,which leads to low accuracy of protocol recognition.An application layer protocol recognition method based on one dimensional residual network and recurrent neural network is proposed.The proposed model consists of one dimensional preactivated residual network(PreResNet) and bidirectional gated recurrent neural network(BiGRU).The PreResNet is used to extract spatial characteristics of the protocol data,and the BiGRU is used to extract temporal characteristics of the protocol data.The attention mechanism is used to extract the key features related to protocol recognition to improve the accuracy of protocol recognition.The proposed method firstly extracts the application layer protocol data from network traffic,and the data is preprocessed and transformed into one dimensional vectors.Then the classification model is trained with the training data and a mature protocol recognition model is obtained.Finally,the trained classification model is used to identify the application layer protocols.Experimental results on public dataset ISCX2012 show that the proposed protocol recognition model has an overall accuracy of 96.87% and an average F value of 96.81%,which are higher than those of other protocol recognition models.

Keywords

• recurrent neural network|residual network|protocol recognition|network security