Abstract-The identification of the nature of the traffic flowing through a TCP/IP network is a relevant target for traffic engineering and security related tasks. Despite the privacy concerns it arises, Deep Packet Inspection (DPI) is one of the most successful current techniques. Nevertheless, the performance of DPI is strongly limited by computational issues related to the huge amount of data it needs to handle, both in terms of number of packets and the length of the packets. One way to reduce the computational overhead with identification techniques is to sample the traffic being monitored. This paper addresses the sensitivity of OpenDPI, one of the most powerful freely available DPI systems, with sampled network traffic. Two sampling techniques are applied and compared: the per-packet payload sampling, and the per-flow packet sampling. Based on the obtained results, some conclusions are drawn to show how far DPI methods could be optimised through traffic sampling.
In REF , the authors study the impact of per-packet payload sampling (i.e., packet truncation) and per-flow packet sampling (i.e., collect only the first packets of a flow) on the performance of OpenDPI.
Performance of OpenDPI in Identifying Sampled Network Traffic
J. Networks
J. Networks
Computer Science