Robust Packet Classification with Field Missing

Abstract

Packet classification shows a key role in kinds of network functions, such as access control, routing, and quality of service (QoS). With the rapid growth of the network size, users have to ignore some fields in packet classification due to resource constraints. In addition, some fields may not always be available in some networks. However, traditional packet classification algorithms can hardly handle packet classification if some fields are missing. In this paper, we propose a novel model to build a robust classifier. In the classifier, we utilize the advantage of Recursive Flow Classification (RFC) in handling fields concurrently. Then, we design a new workflow to deal with field missing based on flows. In addition, two complementary bitmap models are designed to accelerate matching packets to flows, and a buffer mechanism is introduced to further improve the classification accuracy. Our experiments show that the proposed classifier can classify packets with an accuracy of 94%-99.5% when the field missing probability is lower than 0.3.