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.