A survey of techniques for internet traffic classification using machine learning
Swinburne University of Technology
Indexed incrossrefdatacite
Abstract
The research community has begun looking for IP traffic classification techniques that do not rely on `well known' TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007.…
Citation impact
1,629
total citations
- FWCI
- 88.70
- Percentile
- 100%
- References
- 71
Citations per year
Authors
2Topics & keywords
Topics
Keywords
- Computer science
- Traffic classification
- Deep packet inspection
- Context (archaeology)
- The Internet
- Identification (biology)
- Internet traffic
- Open research
No related works found for this paper.