Abstract-Since the early 2000's, the Internet Topology has been frequently described and modeled from the perspective of routers. To this end, alias resolution mechanisms have been developed in order to aggregate all IP interfaces of a router, collected with traceroute, into a single identifier. So far, many active measurement techniques have been considered, often taking advantage of specific features from network protocols. However, a lot of these methods have seen their efficiency decrease over time due to security reinforcements across the Internet.In this paper, we introduce a generic methodology to conduct efficient and scalable alias resolution. It combines the space search reduction of TreeNET (a tool for efficiently discovering subnets) with a fingerprinting process used to assess the feasibility of several state-of-the-art alias resolution methods, using a small, fixed amount of probes. We validate our method along MIDAR on an academic groundtruth and demonstrate that our methodology can achieve similar accuracy while using less probes and discovering subnets in the process. We further evaluate our method with measurements made on PlanetLab towards several distinct ASes of varying sizes and roles in the Internet. The collected data shows that some properties of our fingerprints correlate with each other, hinting some observed profiles could be linked with equipment vendors. Both TreeNET (which implements our methodology) and our dataset are freely available.

Since the late 90's, the Internet topology discovery has been an attractive and important research topic, leading, among others, to multiple probing and data analysis tools developed by the research community. This paper looks at the particular problem of discovering subnets (i.e., a set of devices that are located on the same connection medium and that can communicate directly with each other at the link layer). In this paper, we first show that the use of traffic engineering policies may increase the difficulty of subnet inference. We carefully characterize those difficulties and quantify their prevalence in the wild. Next, we introduce WISE (Wide and lInear Subnet inferencE), a novel tool for subnet inference designed to deal with those issues and able to discover subnets on wide ranges of IP addresses in a linear time. Using two groundtruth networks, we demonstrate that WISE performs better than state-of-the-art tools while being competitive in terms of subnet accuracy. We also show, through large-scale measurements, that the selection of vantage point with WISE does not matter in terms of subnet accuracy. Finally, all our code (WISE, data processing, results plotting) and collected data are freely available.

Over the past two decades, the research community has developed many approaches to study the Internet topology. In particular, starting from 2007, various tools explored the inference of subnets, i.e., sets of devices located on the same connection medium which can communicate directly with each other at the link layer.In this paper, we first discuss how today's traffic engineering policies increase the difficulty of subnet inference. We carefully characterize typical difficulties and quantify them in the wild. Next, we introduce WISE (Wide and lInear Subnet inferencE), a new tool which tackles those difficulties and discovers, in a linear time, large networks subnets. Based on two ground truth networks, we demonstrate that WISE outperforms state-of-theart tools. Then, through large-scale measurements, we show that the selection of a vantage point with WISE has a marginal effect regarding accuracy. Finally, we discuss how subnets can be used to infer neighborhoods (i.e., aggregates of subnets located at most one hop from each other). We discuss how these neighborhoods can lead to bipartite models of the Internet and present validation results and an evaluation of neighborhoods in the wild, using WISE. Both our code and data are freely available.

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