A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification

The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular methods such as port number and payload-based identification exhibit a number of shortfalls. An alternative is to use machine learning (ML) techniques and identify network applications based on per-flow statistics, derived from payload-independent features such as packet length and inter-arrival time distributions. The performance impact of feature set reduction, using Consistencybased and Correlation-based feature selection, is demonstrated on Na

• Type: Journal
• Year: 2006
• Where: CCR
• Authors: Nigel Williams, Sebastian Zander, Grenville J. Armitage
• Keywords: CCR 2006, Classification Accuracy, Naive Bayes, Network Applications