AliasClassifier: A High-Performance Router Alias Classifier

Abstract

The task of router alias resolution for IPv4 networks presents a formidable challenge in the realm of router-level topology inference. Despite the considerable potential exhibited by machine-learning-based alias-resolution methods for IPv4 networks, several constraints impede their effectiveness. These constraints include a low discovery rate of aliased IPs, a failure to account for router aggregation, and a dearth of valid features in current schemes. In this study, we introduce a novel alias resolver, AliasClassifier, which is based on the Random Forest model and the alias triangulation algorithm. This innovative model identifies the key six features from a set of four prevalent routing behaviors that are typically employed to distinguish aliased IPs from non-alienated IPs. Subsequently, the AliasClassifier aggregates aliased IP pairs into routers using an alias triangulation algorithm. Experimental results demonstrate that AliasClassifier excels in discovering aliased IPs in IPv4 networks, boasting a resolution accuracy as high as 94.8% and a recall rate of 40.4%. Its comprehensive performance significantly surpasses that of state-of-the-art alias resolvers such as TreeNET, MLAR, and APPLE. Furthermore, as a typical centralized alias parser, AliasClassifier’s deployment cost is remarkably low. Consequently, AliasClassifier emerges as an ideal tool for router alias resolution in large-scale IPv4 networks.