6RV: Incremental Learning-Based Continuous Identification of IPv6 Router Vendors

The growth of IPv6 networks has led to an expanding number of network routers, but there is not enough research on vendors of these devices. Existing router vendor identification algorithms are based on IPv4, and these static analysis algorithms cannot adapt to dynamically changing IPv6 networks. In this paper, we develop 6RV, a framework for IPv6 router vendors continuous identification based on incremental learning. First, we count the addresses of newly discovered router interfaces every month and obtain router device fingerprints through active probing, and then analyze these data fingerprints through an incremental learning approach to identify the router vendors of new nodes. We validate our identification framework on the ITDK dataset over a period of 8 months, obtaining more than 500K router vendor labels with 94% correctness. Finally, we also analyze the IPv6 router vendor dataset from different perspectives and draw some interesting conclusions.