  Outages from natural disasters, political events, software or hardware
issues, and human error place a huge cost on e-commerce (\$66k per minute at
Amazon). While several existing systems detect Internet outages, these systems
are often too inflexible, with fixed parameters across the whole internet with
CUSUM-like change detection. We instead propose a system using passive data, to
cover both IPv4 and IPv6, customizing parameters for each block to optimize the
performance of our Bayesian inference model. Our poster describes our three
contributions: First, we show how customizing parameters allows us often to
detect outages that are at both fine timescales (5 minutes) and fine spatial
resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show
that, by tuning parameters differently for different blocks, we can scale back
temporal precision to cover more challenging blocks. Finally, we show our
approach extends to IPv6 and provides the first reports of IPv6 outages.
