  The Internet is a complex ecosystem composed of thousands of Autonomous
Systems (ASs) operated by independent organizations; each AS having a very
limited view outside its own network. These complexities and limitations impede
network operators to finely pinpoint the causes of service degradation or
disruption when the problem lies outside of their network. In this paper, we
present Chocolatine, a solution to detect remote connectivity loss using
Internet Background Radiation (IBR) through a simple and efficient method. IBR
is unidirectional unsolicited Internet traffic, which is easily observed by
monitoring unused address space. IBR features two remarkable properties: it is
originated worldwide, across diverse ASs, and it is incessant. We show that the
number of IP addresses observed from an AS or a geographical area follows a
periodic pattern. Then, using Seasonal ARIMA to statistically model IBR data,
we predict the number of IPs for the next time window. Significant deviations
from these predictions indicate an outage. We evaluated Chocolatine using data
from the UCSD Network Telescope, operated by CAIDA, with a set of documented
outages. Our experiments show that the proposed methodology achieves a good
trade-off between true-positive rate (90%) and false-positive rate (2%) and
largely outperforms CAIDA's own IBR-based detection method. Furthermore,
performing a comparison against other methods, i.e., with BGP monitoring and
active probing, we observe that Chocolatine shares a large common set of
outages with them in addition to many specific outages that would otherwise go
undetected.
