An Accurate & Efficient Approach for Traffic Classification Inside Programmable Data Plane - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

An Accurate & Efficient Approach for Traffic Classification Inside Programmable Data Plane

Résumé

In-network traffic classification is a class of innetwork computing that brings significant benefits to the network, i.e., the first line of defence, classification at line rate and fast reaction time. However, it is still challenging to accurately and efficiently classify Internet traffic at an early stage due to a clear trade-off between flow identification time and classification accuracy-both are competing objectives. To this end, we introduce a framework that focuses on deploying an accurate network traffic classifier inside a programmable data plane that can classify the traffic at maximal speed while considering the underlying constraints of the device. Notably, we move from statistical feature-based traffic analysis and argue that traffic flow can be classified using a single feature called sequential packet size information as input. We evaluate our approach by identifying different types of IoT traffic inside a programmable data plane. Our findings demonstrate that accurate and earlystage network traffic classification is achievable with minor use of networking device resources.
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Dates et versions

hal-03820302 , version 1 (18-10-2022)

Identifiants

  • HAL Id : hal-03820302 , version 1

Citer

Muhammad Saqib, Ait Hmitti Zakaria, Halima Elbiaze, Roch Glitho. An Accurate & Efficient Approach for Traffic Classification Inside Programmable Data Plane. IEEE Global Communications Conference, Dec 2022, Rio de Janeiro, Brazil. ⟨hal-03820302⟩
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