Open AccessClassification and identification of unknown network protocols based on CNN and T-SNE
Author(s) -
Jingliang Xue,
YingChun Chen,
Li Ou,
Fēi Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1617/1/012071
Subject(s) - computer science , artificial intelligence , identification (biology) , protocol (science) , adaptability , convolutional neural network , robustness (evolution) , machine learning , artificial neural network , cluster analysis , data mining , medicine , ecology , biochemistry , chemistry , botany , alternative medicine , pathology , gene , biology
With the continuous development of users’ demands and network technology, more and more new network protocols emerge, which poses great challenges to network protocol classification and identification. An artificial intelligence method was used to explore autonomous classification and identification of unknown network protocols in this paper in order to reduce the time and labor cost of network protocol classification and identification. In this paper, firstly, the network traffic was converted into grayscale images, and through transfer learning, the Convolutional Neural Networks (CNN) pre-trained model was used to extract the protocol features, so as to reduce the time and the amount of labeled data needed for the artificial neural network training. Finally, with the improved unsupervised hybrid clustering algorithm based on T-SNE and K-means, the types and number of protocols were autonomously identified and the network traffic was classified simultaneously. In this way, we can identify unknown protocols without prior knowledge and the protocol identification adaptability for big data was also greatly improved. Experimental results show this method has high accuracy and robustness in identifying unknown network protocols.