On Internet Traffic Classification: A Two-Phased Machine Learning Approach
- Submitting institution
-
University of Plymouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2070
- Type
- D - Journal article
- DOI
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10.1155/2016/2048302
- Title of journal
- Journal of Computer Networks and Communications
- Article number
- 2048302
- First page
- -
- Volume
- 2016
- Issue
- -
- ISSN
- 2090-7141
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- As the internet increases in traffic volume and complexity, there is an increasing need to categorise traffic in a rapid, effective, and accurate manner in order to timely and appropriately respond to application requirements. This paper is important because it provides an efficient and accurate alternative for high traffic classification using a lightweight input data collection and processing, and demanding low computational effort. Using its combination of unsupervised clustering and a C5.0 classifier makes it very appropriate for typical high-speed stub networks. The proposed approach is very effective, classifying with an accuracy of 96% and specificity of 98-99%.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -