论文标题
100,000,000网络数据包的多阶段分析和扩展关系
Multi-Temporal Analysis and Scaling Relations of 100,000,000,000 Network Packets
论文作者
论文摘要
我们的社会从未更依赖计算机网络。有效利用网络需要详细了解网络流量的正常背景行为。网络的大规模测量在计算上具有挑战性。在交互式超级计算和Graphblas Hypersparse层次交通矩阵的先前工作的基础上，我们开发了一种有效的方法来计算各种时间尺度上的各种流媒体网络数量。将这些方法应用于在网络网关上收集的100,000,000,000个匿名源污点对，显示了许多以前未观察到的缩放关系。这些观察结果为正常网络背景流量提供了新的见解，可用于异常检测，AI功能工程以及测试流网络的理论模型。
Our society has never been more dependent on computer networks. Effective utilization of networks requires a detailed understanding of the normal background behaviors of network traffic. Large-scale measurements of networks are computationally challenging. Building on prior work in interactive supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have developed an efficient method for computing a wide variety of streaming network quantities on diverse time scales. Applying these methods to 100,000,000,000 anonymized source-destination pairs collected at a network gateway reveals many previously unobserved scaling relationships. These observations provide new insights into normal network background traffic that could be used for anomaly detection, AI feature engineering, and testing theoretical models of streaming networks.
