论文标题
边缘上的Graphblas：网络流量的匿名高性能流
GraphBLAS on the Edge: Anonymized High Performance Streaming of Network Traffic
论文作者
论文摘要
远程检测是许多操作域（陆地，海洋，海底，空气，空间，..）的防御基石。在网络域中，远距离检测需要分析来自各种观测站和哨所的大量网络流量。通过以可保护隐私的快速分析格式提供重要的数据压缩，可以在边缘网络设备上建造匿名的Hypersparse交通矩阵可以成为关键推动因素。 Graphblas非常适合构建和分析匿名的Hypersparse交通矩阵。 Graphblas在Accolade Technologies Edge网络设备上的性能在几乎差的案例流量方案中使用了连续的CAIDA望远镜DarkNet数据包。探索了不同数量的流量缓冲区，线程和处理器内核的性能。匿名的Hyperparse交通矩阵可以以每秒50,000,000多个数据包的速度构建；超过典型的400千兆网络链接。这种性能表明，匿名的Hyperparse流量矩阵可以在具有最小计算资源的边缘网络设备上计算，并且可以成为此类设备的可行数据产品。
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.
