The Application of Deep Learning on Network Traffic Classification

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Lotfollahi, Mohammad | 2017

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 49848 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Jafari Siavoshani, Mahdi
Abstract:
Almost all of the network traffic classification systems use pre-defined extracted features by the experts in computer network. These features include regular expressions, port number, information in the header of different layers and statistical feature of the flow. The main problem of the traffic analysis and anomaly detection system lies in finding appropriate features. The feature extraction is a time consuming process which needs an expert to be done. It is notable that the classification of special kinds of traffic like encrypted traffic is impossible using some subset of mentioned features.The lack of integration in feature detection and classification is also another important issue which need to be considered. We propose a solution based on deep learning and artificial neural networks to solve aforementioned problems.We use autoencoders and convolutional networks to do classification and characterization of network the traffic. The state-ofthe- art results achieved by our solution show that this approach is capable to identify encrypted traffic and surpass the accuracy achieved by almost every classical method in this area of research
Keywords:
Feature Extraction ; Deep Learning ; Artificial Neural Network ; Network Traffic ; Traffic Identification