6SEVEN: System for EValuating IPv6 ENumeration algorithms

Abstract

The vast, sparsely populated, and often ephemeral IPv6 address space makes discovering active addresses challenging. In response, the community has developed over thirty different IPv6 Target Generation Algorithms (TGAs). TGAs learn addressing structure from seed (training) datasets, build a representative model, and generate candidate addresses. Unfortunately, the existing literature employs a wide variety of input seeds, data cleansing, and metrics of success that prevent ready comparison. Toward making TGA evaluation consistent, we present 6SEVEN, an extensible framework that hosts TGAs as plugins and links them to shared data cleaning, probing, dealiasing, and result tabulation components. We port as 6SEVEN plugins eight popular TGAs and demonstrate a controlled case study. Our results show that the performance of these TGAs varies substantially due to factors independent of the main algorithm—especially the seed set composition, dealiasing, and tabulation procedures. We observe tradeoffs between desirable features of TGA performance, including yield and exploration. We envision 6SEVEN as an enabling community resource to advance the science of current and future TGAs, and, by extension, IPv6 measurement.