University of Twente Student Theses
Evaluating Random Forest Performance on Packet- and Flow-Level Features for Network Traffic Classification
Luning, M. (2025) Evaluating Random Forest Performance on Packet- and Flow-Level Features for Network Traffic Classification.
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| Abstract: | This paper evaluates the performance of Random Forest models for network traffic classification using two different feature sets, namely packet- and flow-level features. These features are extracted from an existing Internet traffic capture and label the data using histogram-based methods. Two distinct models are trained, one on packet-level features and the other on flow-level features. The performance of the models is assessed based on accuracy, precision, and recall, and a feature importance analysis is conducted. The results show the potential of Random Forest for effective network traffic classification and provide insights into the importance of packet- and flow-level features for such tasks. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/105089 |
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