Traffic Classification Based on Zero-Length Packets

Network traffic classification is fundamental to network management and its performance. However, traditional approaches for traffic classification, which were designed to work on a dedicated hardware at very high line rates, may not function well in a virtual software-based environment. In this paper, we devise a novel fingerprinting technique that can be utilized as a software-based solution which enables machine-learning-based classification of ongoing flows. The suggested scheme is very simple to implement and requires minimal resources, yet attains very high accuracy. Specifically, for TCP flows, we suggest a fingerprint that is based on zero-length packets, hence enables a highly efficient sampling strategy which can be adopted with a single content-addressable memory rule. The suggested fingerprinting scheme is robust to network conditions such as congestion, fragmentation, delay, retransmissions, duplications, and losses and to varying processing capabilities. Hence, its performance is essentially independent of placement and migration issues, and thus yields an attractive solution for virtualized software-based environments. We suggest an analogous fingerprinting scheme for user datagram protocol traffic, which benefits from the same advantages as the TCP one and attains very high accuracy as well. Results show that our scheme correctly classified about 97% of the flows on the dataset tested, even on encrypted data.