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 …
%0 Journal Article
%1 gray2020beacons
%A Gray, Caitlin
%A Mosig, Clemens
%A Bush, Randy
%A Pelsser, Cristel
%A Roughan, Matthew
%A Schmidt, Thomas C
%A Wahlisch, Matthias
%D 2020
%K imported topic_earthenvironment
%P 492-505
%T BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap Damping
%X 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 …
@article{gray2020beacons,
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 …},
added-at = {2024-11-29T11:48:11.000+0100},
author = {Gray, Caitlin and Mosig, Clemens and Bush, Randy and Pelsser, Cristel and Roughan, Matthew and Schmidt, Thomas C and Wahlisch, Matthias},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/26838585a369adba6bb19efe10364da6b/joum576e},
citation = {Proceedings of the ACM Internet Measurement Conference, 492-505, 2020},
conference = {Proceedings of the ACM Internet Measurement Conference},
interhash = {c8367ddf546c7075e46e8dd4e35c21e8},
intrahash = {6838585a369adba6bb19efe10364da6b},
keywords = {imported topic_earthenvironment},
pages = {492-505},
timestamp = {2024-11-29T11:48:11.000+0100},
title = {BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap Damping},
year = 2020
}
