  The Internet has become a critical component of modern civilization requiring
scientific exploration akin to endeavors to understand the land, sea, air, and
space environments. Understanding the baseline statistical distributions of
traffic are essential to the scientific understanding of the Internet.
Correlating data from different Internet observatories and outposts can be a
useful tool for gaining insights into these distributions. This work compares
observed sources from the largest Internet telescope (the CAIDA darknet
telescope) with those from a commercial outpost (the GreyNoise honeyfarm).
Neither of these locations actively emit Internet traffic and provide distinct
observations of unsolicited Internet traffic (primarily botnets and scanners).
Newly developed GraphBLAS hyperspace matrices and D4M associative array
technologies enable the efficient analysis of these data on significant scales.
The CAIDA sources are well approximated by a Zipf-Mandelbrot distribution. Over
a 6-month period 70\% of the brightest (highest frequency) sources in the CAIDA
telescope are consistently detected by coeval observations in the GreyNoise
honeyfarm. This overlap drops as the sources dim (reduce frequency) and as the
time difference between the observations grows. The probability of seeing a
CAIDA source is proportional to the logarithm of the brightness. The temporal
correlations are well described by a modified Cauchy distribution. These
observations are consistent with a correlated high frequency beam of sources
that drifts on a time scale of a month.
