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Title:
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A DEEP LEARNING BASED TRAFFIC CLASSIFICATION IN SOFTWARE DEFINED NETWORKING |
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Author(s):
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Rahma Hammedi |
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ISBN:
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978-989-8704-27-6 |
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Editors:
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Miguel Baptista Nunes, Pedro Isaías and Philip Powell |
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Year:
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2021 |
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Edition:
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Single |
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Keywords:
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SDN, Traffic Classification, Deep Learning, Artificial Neural Network |
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Type:
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Full |
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First Page:
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89 |
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Last Page:
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96 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Moving from legacy internet applications, such as web, and e-mail, to dynamic complex applications, such as video
streaming, and file sharing, improves the underlying network architectures to provide new services with high QoS
requirements. Thus, a new network paradigm is developed and deployed; Software Defined Networking (SDN). As a result
of the significant impact of using the internet, network traffic is growing up, and the network itself is becoming more
overloaded. Therefore, new tools that can cover new requirements for high-quality services are becoming mandatory.
Inherently, traffic classification (TC) has gained continuous interest as an important decision- centric approach to deliver
the quality of service. The current main challenge is how to classify flows efficiently. In this work, a new method of
classification for incoming flows is proposed. It is based on Deep learning and consists of using the MultiLayer Perceptron
Model (MLP) to classify flows according to its constraints throughput and delay. Experimental results prove that the
proposed approach outperforms parallel solutions in terms of precision. It can classify traffic with more than 97.7%
precision compared to the Linear Regression classification and the Fuzzy Decision- tree model. |
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