Archive for October, 2020

On Measuring RPKI Relying Parties

John Kristoff, Randy Bush, Chris Kanich, George Michaelson, Amreesh Phokeer, Thomas Schmidt, Matthias Wählisch. On Measuring RPKI Relying Parties, ACM IMC 2020

On Measuring RPKI Relying Parties

In this paper, we introduce a framework to observe RPKI relying parties (i.e., those that fetch RPKI data from the distributed repository) and present insights into this ecosystem for the first time. Our longitudinal study of data gathered from three RPKI certification authorities (AFRINIC, APNIC, and our own CA) identifies different deployment models of relying parties and (surprisingly) prevalent inconsistent fetching behavior that affects Internet routing robustness. Our results reveal nearly 90% of relying parties are unable to connect to delegated publication points under certain conditions, which leads to erroneous invalidation of IP prefixes and likely widespread loss of network reachability.

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

Caitlin Gray, Clemens Mosig, Randy Bush, Cristel Pelsser, Matthew
Roughan, Thomas Schmidt, Matthias Wählisch . BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap Damping, ACM IMC 2020

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 tomog- raphy, 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.

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