Network management

By Laurent Bernaille, Renata Teixeira, Ismael Akodkenou, Augustin Soule, Kave Salamatian. 1994

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Citations in the corpus

A survey of techniques for internet traffic classification using machine learning (2008, methodology): “2) Directional neutrality: The assumption that application flows are bi-directional, and the application’s direction may be inferred prior to classification, permeates many of the works published to date ( [14] [48] [53] [46] [68]).”
Network Traffic Classification Using Correlation Information (2013, methodology): “[9] applied the k-means algorithm to traffic clustering and labeled the clusters to applications using a payload analysis tool.”
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic (2020, background): “[13] stressed the importance of fast recognition of apps in network traffic and suggested the clustering of TCP flows based on their first five messages.”
Early application identification (2006, background): “In [5], we argue that it is possible to perform traffic classification based on the inspection of just the first few packets of a TCP connection (typically, the first four or five packets).”
A brief survey on sequence classification (2010, background): “[9] show that by only observing the first five packages of a TCP connection, the application associated with the traffic flow can be classified.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “These sub-flows can be extended synthetically [55] or derived from a small sliding window over each flow [337].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “Another improved and faster approach was proposed in 2006 [174], which examines the size of the first five packets and determines the application correctly using unsupervised learning techniques.”