Traffic Classification


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The research on network traffic classification/identification (i.e. associating traffic flows to the applications that generated them) has recently become very active. This happened because, lately, the traditional approach of relying on transport-level protocol ports has become largely unreliable, pushing the search for alternative techniques.
At first, research and industry focused on approaches based on packet-payload inspection. However, such techniques present several drawbacks in realistic scenarios, e.g.: (i) their large computational cost makes it difficult to use them on high-bandwidth links; (ii) requiring full access to packet payload poses privacy concerns; (iii) they are typically unable to cope with traffic encryption and protocol obfuscation techniques.
For these reasons, the research community started proposing traffic classification approaches that consider other properties of network traffic, typically by adopting statistical and machine-learning approaches. The current state of the art is quite rich and heterogeneous, but it is notable a lack of shared tools and reference data that would allow a better assessment of the present research achievements.
Therefore the activities stimulated by the TMA COST action are not only focused on improving and designing novel techniques but also on studies and on the development of tools and publication of reference data that would allow researchers to better evaluate and compare the available techniques in realistic conditions.

Subjects of interest

  • Novel techniques and approaches to traffic classification/identification
  • Identification of new-generation applications and proprietary protocols
  • Ground-Truth Techniques
  • Traffic classification of sampled traffic
  • Traffic classification of encrypted Traffic

COST-TMA events related to traffic classification

Bookmarks

Research groups working on Traffic Classification in COST-TMA