Spatial Temporal Analysis of 40,000,000,000,000 Internet Darkspace Packets

TL;DR

This paper analyzes an unprecedented dataset of 40 trillion Internet darkspace packets from 2019-2020, revealing new insights into unsolicited traffic patterns and their stability amid the COVID-19 pandemic.

Contribution

It introduces a large-scale analysis of darkspace Internet traffic using GraphBLAS hierarchical hypersparse matrices, uncovering new scaling relations and traffic behaviors.

Findings

Unprecedented volume of darkspace traffic analyzed

Significant increase in unsolicited traffic during COVID-19

Stable scaling relations despite traffic volume changes

Abstract

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.