  Long range detection is a cornerstone of defense in many operating domains
(land, sea, undersea, air, space, ..,). In the cyber domain, long range
detection requires the analysis of significant network traffic from a variety
of observatories and outposts. Construction of anonymized hypersparse traffic
matrices on edge network devices can be a key enabler by providing significant
data compression in a rapidly analyzable format that protects privacy.
GraphBLAS is ideally suited for both constructing and analyzing anonymized
hypersparse traffic matrices. The performance of GraphBLAS on an Accolade
Technologies edge network device is demonstrated on a near worse case traffic
scenario using a continuous stream of CAIDA Telescope darknet packets. The
performance for varying numbers of traffic buffers, threads, and processor
cores is explored. Anonymized hypersparse traffic matrices can be constructed
at a rate of over 50,000,000 packets per second; exceeding a typical 400
Gigabit network link. This performance demonstrates that anonymized hypersparse
traffic matrices are readily computable on edge network devices with minimal
compute resources and can be a viable data product for such devices.
