The Internet has never been more important to our society, and understanding the behavior of the Internet is essential. The Center for Applied Internet Data Analysis (CAIDA) Telescope observes a continuous stream of packets from an unsolicited darkspace representing 1/256 of the Internet. During 2019 and 2020 over 40,000,000,000,000 unique packets were collected representing the largest ever assembled public corpus of Internet traffic. Using the combined resources of the Supercomputing Centers at UC San Diego, Lawrence Berkeley National Laboratory, and MIT, the spatial temporal structure of anonymized source-destination pairs from the CAIDA Telescope data has been analyzed with GraphBLAS hierarchical hypersparse matrices. These analyses provide unique insight on this unsolicited Internet darkspace traffic with the discovery of many previously unseen scaling relations. The data show a significant sustained increase in unsolicited traffic corresponding to the start of the COVID19 pandemic, but relatively little change in the underlying scaling relations associated with unique sources, source fan-outs, unique links, destination fan-ins, and unique destinations. This work provides a demonstration of the practical feasibility and benefit of the safe collection and analysis of significant quantities of anonymized Internet traffic.
翻译:应用互联网数据分析中心(CAIDA)望远镜观察了来自一个代表因特网1/256的未经请求的暗层连续不断的包裹流,2019年和2020年期间收集了40 000 000 000多件独特的包裹,代表了有史以来最大的因特网交通公共设施。利用了圣地亚哥哥伦比亚大学超级计算中心、伯克利实验室和麻省理工大学的合并资源,利用了CAIDA Telescope数据中匿名源 - 目的地的对子的空间时间结构,与GregBLAS的高度偏差进行了分析,这些分析为这种未经请求的因特网暗层交通提供了独特的洞察力,发现了许多以前看不见的缩放关系。数据显示,与COVID19大流行开始时相对而言,未经请求的交通量持续大幅增加,但与独特来源、源扇断、独特链接、目的地粉丝网和独特目的地有关的基本规模关系变化相对小。这项工作展示了安全收集和分析大量匿名互联网交通的实际可行性和益处。