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

In-network traffic classification is a class of in-network 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.

Automated summary

In-network traffic classification is a category of in-network computing, where an accurate network traffic classifier is deployed inside a programmable data plane to classify traffic at maximum speed while considering device constraints. Traffic flow can be classified using a single feature, sequential packet size information, to achieve accurate and early-stage network traffic classification with minimal use of networking device resources.