  Defending community-owned cyber space requires community-based efforts.
Large-scale network observations that uphold the highest regard for privacy are
key to protecting our shared cyberspace. Deployment of the necessary network
sensors requires careful sensor placement, focusing, and calibration with
significant volumes of network observations. This paper demonstrates novel
focusing and calibration procedures on a multi-billion packet dataset using
high-performance GraphBLAS anonymized hypersparse matrices. The run-time
performance on a real-world data set confirms previously observed real-time
processing rates for high-bandwidth links while achieving significant data
compression. The output of the analysis demonstrates the effectiveness of these
procedures at focusing the traffic matrix and revealing the underlying stable
heavy-tail statistical distributions that are necessary for anomaly detection.
A simple model of the corresponding probability of detection ($p_{\rm d}$) and
probability of false alarm ($p_{\rm fa}$) for these distributions highlights
the criticality of network sensor focusing and calibration. Once a sensor is
properly focused and calibrated it is then in a position to carry out two of
the central tenets of good cybersecurity: (1) continuous observation of the
network and (2) minimizing unbrokered network connections.
