BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap Damping

Caitlin Gray , Clemens Mosig , Randy Bush , Cristel Pelsser , M. Roughan , Thomas Schmidt and Matthias Wählisch

Internet Measurement Conference (IMC) January 2020 Pages 492--505
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Abstract

Pinpointing autonomous systems which deploy specific inter-domain techniques such as Route Flap Damping (RFD) or Route Origin Validation (ROV) remains a challenge today. Previous approaches to detect per-AS behavior often relied on heuristics derived from passive and active measurements. Those heuristics, however, often lacked accuracy or imposed tight restrictions on the measurement methods. We introduce an algorithmic framework for network tomography, BeCAUSe, which implements Bayesian Computation for Autonomous Systems. Using our original combination of active probing and stochastic simulation, we present the first study to expose the deployment of RFD. In contrast to the expectation of the Internet community, we find that at least 9% of measured ASs enable RFD, most using deprecated vendor default configuration parameters. To illustrate the power of computational Bayesian methods we compare BeCAUSe with three RFD heuristics. Thereafter we successfully apply a generalization of the Bayesian method to a second challenge, measuring deployment of ROV.

Publication Details

Publication Type
Conference Paper
Publication Date
January 2020
Published In
Internet Measurement Conference (IMC)
Pages
492--505
Publisher
ACM
Location
Virtual Event, USA
Digital Object Identifier (DOI)
10.1145/3419394.3423624

Suggested citation

Caitlin Gray, Clemens Mosig, Randy Bush, Cristel Pelsser, M. Roughan, Thomas Schmidt, and Matthias Wählisch. 2020. BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap Damping. In Internet Measurement Conference (IMC). ACM, Virtual Event, USA, 492–505. https://doi.org/10.1145/3419394.3423624

BibTeX Citation

@inproceedings{Gray2020a,
	title        = {BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap Damping},
	author       = {Gray, Caitlin and Mosig, Clemens and Bush, Randy and Pelsser, Cristel and Roughan, M. and Schmidt, Thomas and Wählisch, Matthias},
	year         = 2020,
	month        = jan,
	booktitle    = {Internet Measurement Conference (IMC)},
	location     = {Virtual Event, USA},
	publisher    = {ACM},
	pages        = {492--505},
	doi          = {10.1145/3419394.3423624},
	url          = {http://icube-publis.unistra.fr/4-GCBP20},
	abstract     = {Pinpointing autonomous systems which deploy specific inter-domain techniques such as Route Flap Damping (RFD) or Route Origin Validation (ROV) remains a challenge today. Previous approaches to detect per-AS behavior often relied on heuristics derived from passive and active measurements. Those heuristics, however, often lacked accuracy or imposed tight restrictions on the measurement methods. We introduce an algorithmic framework for network tomography, BeCAUSe, which implements Bayesian Computation for Autonomous Systems. Using our original combination of active probing and stochastic simulation, we present the first study to expose the deployment of RFD. In contrast to the expectation of the Internet community, we find that at least 9% of measured ASs enable RFD, most using deprecated vendor default configuration parameters. To illustrate the power of computational Bayesian methods we compare BeCAUSe with three RFD heuristics. Thereafter we successfully apply a generalization of the Bayesian method to a second challenge, measuring deployment of ROV.},
	groups       = {International Conferences},
	keywords     = {Hamiltonian Monte Carlo, Metropolis-Hasting, RFD, RPKI},
	numpages     = 14,
	x-international-audience = {Yes},
	x-language   = {EN}
}

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