imc – ANT Research News

Our new paper “Inferring Changes in Daily Human Activity from Internet Response” will appear at The 2023 Internet Measurement Conference (IMC 2023).

From the abstract:

Network traffic is often diurnal, with some networks peaking during the workday and many homes during evening streaming hours. Monitoring systems consider diurnal trends for capacity planning and anomaly detection. In this paper, we reverse this inference and use diurnal network trends and their absence to infer human activity. We draw on existing and new ICMP echo-request scans of more than 5.2M /24 IPv4 networks to identify diurnal trends in IP address responsiveness. Some of these networks are change-sensitive, with diurnal patterns correlating with human activity. We develop algorithms to clean this data, extract underlying trends from diurnal and weekly fluctuation, and detect changes in that activity. Although firewalls hide many networks, and Network Address Translation often hides human trends, we show about 168k to 330k (3.3–6.4% of the 5.2M) /24 IPv4 networks are change-sensitive. These blocks are spread globally, representing some of the most active 60% of 2 × 2◦ geographic gridcells, regions that include 98.5% of ping-responsive blocks. Finally, we detect interesting changes in human activity. Reusing existing data allows our new algorithm to identify changes, such as Work-from-Home due to the global reaction to the emergence of Covid-19 in 2020. We also see other changes in human activity, such as national holidays and government-mandated curfews. This ability to detect trends in human activity from the Internet data provides a new ability to understand our world, complementing other sources of public information such as news reports and wastewater virus observation.

The human-activity changes for 2020h1 by continent. It shows the global count of downward trends in changes for each continent over six months. Although aggregated, we see several trends. First, the large percentage of changes in Asia around 2020-01-20 (at (i)) might correspond to the Spring Festival, celebrated widely in many Asian countries and regions. Most of the rest of the world showed significant changes around 2020-03-20 (at (ii) and (iii)), corresponding to initial Covid pandemic control measures.

This paper is a joint work of Xiao Song from USC, Guillermo Baltra from USC, and John Heidemann from USC/ISI. Datasets from this paper can be found at https://ant.isi.edu/datasets/ip_accumulation. This work was supported by NSF (MINCEQ, NSF 2028279; EIEIO CNS-2007106; and InternetMap (CSN-2212480).

Asma Enayet will present her poster “Internet Outage Detection Using Passive Analysis” by Asma Enayet and John Heidemann at ACM Internet Measurement Conference, Nice, France from October 25-27th, 2022.

We expect the ACM poster abstract (without the poster) to appear at https://doi.org/10.1145/3517745.3563032 in October 2022.

We are making a report available now with the poster abstract and poster at https://doi.org/10.48550/arXiv.2209.13767 as a pre-print.

From the abstract:

Outages from natural disasters, political events, software or hardware issues, and human error place a huge cost on e-commerce ($66k per minute at Amazon). While several existing systems detect Internet outages, these systems are often too inflexible, with fixed parameters across the whole internet with CUSUM-like change detection. We instead propose a system using passive data, to cover both IPv4 and IPv6, customizing parameters for each block to optimize the performance of our Bayesian inference model. Our poster describes our three contributions: First, we show how customizing parameters allows us often to detect outages that are at both fine timescales (5 minutes) and fine spatial resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show that, by tuning parameters differently for different blocks, we can scale back temporal precision to cover more challenging blocks. Finally, we show our approach extends to IPv6 and provides the first reports of IPv6 outages.

**IPv6 Coverage**: our source of passive data (B-Root) is incomplete, but it provides similar coverage in both IPv4 and IPv6.

**IPv6 Outages**: Outage rate for IPv6 (12%) is greater than for IPv4 (5.5%) —IPv6 reliability can improve.

This work was supported by NSF grant CNS-2007106 (EIEIO).