A Survey of Payload-Based Traffic Classification Approaches

By M. Finsterbusch, Chris Richter, Eduardo Rocha, Jean-Alexander Müller, K. Hanssgen. IEEE Communications Surveys and Tutorials, 2014

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/de9692aafc9e1b61af41019366b46eff7c8a329a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohammad Lotfollahi
2. Gang Xiong
3. Zhen Li
4. Chang Liu
5. Ming Zhu
6. Wei Wang
7. Mahdi Jafari Siavoshani
8. Zigang Cao

In Computer Networks and Communications

1. Mohammad Lotfollahi
2. Wei Wang
3. Mahdi Jafari Siavoshani
4. Ramin Shirali Hossein Zade
5. Pavel Čeleda
6. Milan Cermák
7. Xuewen Zeng
8. Martin Drašar

In Hardware and Architecture

1. Mohammad Lotfollahi
2. Mahdi Jafari Siavoshani
3. Ramin Shirali Hossein Zade
4. Pavel Čeleda
5. Mohammdsadegh Saberian

Citations in the corpus

• Malware traffic classification using convolutional neural network for representation learning (2017, background): “[8] summarized current main DPI-based traffic classification methods.”
• A survey of methods for encrypted traffic classification and analysis (2015, background): “Although there has been a lot of research on traffic classification in the past decade [25, 58, 71, 20, 32], most of the existing surveys do not explicitly consider research which targets encrypted traffic.”
• Deep packet: a novel approach for encrypted traffic classification using deep learning (2017, background): “, see [13], [15], [42] and the references therein).”
• FS-Net: A Flow Sequence Network For Encrypted Traffic Classification (2019, methodology): “Payloadbased method [17] uses the specific signature strings in the payload for matching.”