Pushing the Boundaries with bdrmapIT

Marder, Alexander; Luckie, Matthew; Dhamdhere, Amogh; Huffaker, Bradley; claffy, kc; Smith, Jonathan M.

doi:10.1145/3278532.3278538

Cited by 51 publications

(11 citation statements)

References 26 publications

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“…The accuracy needs to be improved Pansiot et al 17 Router-to-AS algorithm based on probabilistic and empirical IP allocation rules The scope of application of the method is limited Motamedi et al 18 IXP-assigned IPs and Valley-free heuristics, and some improved previous heuristics There are cases where the owner AS cannot be inferred for the router Marder et al 11 BdrmapIT: combine bdrmap 12 and MAP-IT 33 Constraints available in annotating last hops are not enough Analysis of bdrmapIT. The bdrmapIT 11 is a classical approach that implements router ownership inference at the Internet scale. When it maps AS boundaries on two traceroute data sets, the accuracy is more than 90%.…”

Section: Categories Authors Methods Drawbacksmentioning

confidence: 99%

“…When it maps AS boundaries on two traceroute data sets, the accuracy is more than 90%. bdrmapIT 11 method mainly includes three phases: constructing the graph, annotating the last hops, and graph refinement. A router-level directed topology is established in the graph construction phase based on the IP-level topology from traceroutes after alias resolution.…”

Section: Categories Authors Methods Drawbacksmentioning

confidence: 99%

“…1. The bdrmapIT 11 combines bdrmap 12 and MAP-IT 33 to adapt bdrmap which targets a network to the iterative improvement framework of MAP-IT. This reduces bdrmap's requirements for the number and location of VPs, and expands its scope of application (from a specific network to any number of networks).…”

Section: Categories Authors Methods Drawbacksmentioning

confidence: 99%

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Inferring router ownership based on the classification of intra- and inter-domain links

Liu¹,

Zhao²,

Guo³

2023

Sci Rep

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Research on router ownership inference is central to many Internet studies, such as network failure diagnosis, network boundary identification, network resilience assessment, and inter-domain congestion detection. The existing router ownership inference method bdrmapIT has relatively few constraints on routers at the end of traceroute paths, resulting in some inference errors. In this paper, a router ownership inference method based on the classification of intra- and inter-domain links is proposed. In this method, the differentiating Internet Protocol (IP) address vector distance feature, the autonomous system relationship feature of the IP link, and the fan-in and fan-out features are designed to support the discrimination of IP link types. The use of additional information derived from the link type enriches the basis for router ownership inference and improves the accuracy of the inference result. Experimental results show that the accuracy reaches 96.4% and 94.6% on the two verification sets, respectively, which is 3.2–11.2% better than the existing typical methods.

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Section: Categories Authors Methods Drawbacksmentioning

confidence: 99%

Section: Categories Authors Methods Drawbacksmentioning

confidence: 99%

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Inferring router ownership based on the classification of intra- and inter-domain links

Liu¹,

Zhao²,

Guo³

2023

Sci Rep

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“…Past work on Internet mapping was largely confined to specific layers of the Internet and did not support crosslayer mapping [50,75]; for example, mapping of physical cables [52,70,71], interfaces [44,59,82,83], routers [62,67,73,74], PoPs [53,64,79,81], and ASes [43,48,78]. Recently, iGDB [45] put forward the first cross-layer mapping framework for the Internet.…”

Section: Introductionmentioning

confidence: 99%

Nautilus: A Framework for Cross-Layer Cartography of Submarine Cables and IP Links

Ramanathan¹,

Jyothi²

2023

Preprint

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Submarine cables constitute the backbone of the Internet. However, these critical infrastructure components are vulnerable to several natural and man-made threats, and during failures, are difficult to repair in their remote oceanic environments. In spite of their crucial role, we have a limited understanding of the impact of submarine cable failures on global connectivity, particularly on the higher layers of the Internet.In this paper, we present Nautilus, a framework for crosslayer cartography of submarine cables and IP links. Using a corpus of public datasets and Internet cartographic techniques, Nautilus identifies IP links that are likely traversing submarine cables and maps them to one or more potential cables. Nautilus also gives each IP to cable assignment a prediction score that reflects the confidence in the mapping. Nautilus generates a mapping for 3.05 million and 1.42 million IPv4 and IPv6 links respectively, covering 91% of all active cables. In the absence of ground truth data, we validate Nautilus mapping using three techniques: analyzing past cable failures, using targeted traceroute measurements, and comparing with public network maps of two operators.

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“…The Ref. [9] combines the content of Ref. [5,7] and adds the voting mechanism to identify the boundary of AS.…”

Section: Introductionmentioning

confidence: 99%

A City-Level Boundary Nodes Identification Algorithm Based on Bidirectional Approaching

2021

KSII TIIS

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Existing city-level boundary nodes identification methods need to locate all IP addresses on the path to differentiate which IP is the boundary node. However, these methods are susceptible to time-delay, the accuracy of location information and other factors, and the resource consumption of locating all IPes is tremendous. To improve the recognition rate and reduce the locating cost, this paper proposes an algorithm for city-level boundary node identification based on bidirectional approaching. Different from the existing methods based on time-delay information and location results, the proposed algorithm uses topological analysis to construct a set of candidate boundary nodes and then identifies the boundary nodes. The proposed algorithm can identify the boundary of the target city network without highprecision location information and dramatically reduces resource consumption compared with the traditional algorithm. Meanwhile, it can label some errors in the existing IP address database. Based on 45,182,326 measurement results from Zhengzhou, Chengdu and Hangzhou in China and New York, Los Angeles and Dallas in the United States, the experimental results show that: The algorithm can accurately identify the city boundary nodes using only 20.33% location resources, and more than 80.29% of the boundary nodes can be mined with a precision of more than 70.73%.

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Pushing the Boundaries with bdrmapIT

Cited by 51 publications

References 26 publications

Inferring router ownership based on the classification of intra- and inter-domain links

Inferring router ownership based on the classification of intra- and inter-domain links

Nautilus: A Framework for Cross-Layer Cartography of Submarine Cables and IP Links

A City-Level Boundary Nodes Identification Algorithm Based on Bidirectional Approaching

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