Network protocol recognition based on convolutional neural network

How to correctly acquire the appropriate features is a primary problem in network protocol recognition field. Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy, a network protocol recognition method based on Convolutional Neural N...

Full description

Saved in:
Bibliographic Details
Published in:China communications 2020-04, Vol.17 (4), p.125-139
Main Authors: Feng, Wenbo, Hong, Zheng, Wu, Lifa, Fu, Menglin, Li, Yihao, Lin, Peihong
Format: Article
Language:English
Subjects:
Classification algorithms
classification model
convolutional neural network
Deep learning
Feature extraction
network flow
Neurons
protocol recognition
Protocols
Telecommunication traffic
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:How to correctly acquire the appropriate features is a primary problem in network protocol recognition field. Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy, a network protocol recognition method based on Convolutional Neural Network (CNN) is proposed. The method utilizes deep learning technique, and it processes network flows automatically. Firstly, normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of CNN. Then, an improved classification model named PtrCNN is built, which can automatically extract the appropriate features of network protocols. Finally, the classification model is trained to recognize the network protocols. The proposed approach is compared with several machine learning methods. Experimental results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time.
ISSN:1673-5447
DOI:10.23919/JCC.2020.04.012