Examining internet blackouts through public data sources
Pulling the plug on the internet is one of the ways that governments around the world attempt to exert control over the flow of information.
While the Open Observatory of Network Interference (OONI) project has developed numerous software tests for examining different forms of internet censorship (such as the blocking of websites, instant messaging apps, and censorship circumvention tools), we currently do not have tests that are designed to examine internet blackouts, when the internet as a whole is rendered inaccessible within a location.
Over the last months we received many reports relating to internet blackouts in various countries around the world. In some of these countries we had probes running OONI tests, but merely asserting that an internet blackout had occurred just because we stopped receiving measurements probably wouldn’t have been accurate. As such, we started to refer to other public data sources that could help us gain a better understanding of potential network disruptions in countries where internet blackouts were reported by locals.
In this post we outline some basics from our methodology when examining internet blackouts through public data sources.
Identifying data sources
Our main criterion for identifying data sources that can help shed light on network disruptions is that they collect and publish a large volume of internet traffic from as many countries around the world as possible (including, of course, the country where an internet blackout has been reported), or that they provide insight into the routing within networks.
Some public data sources, such as Google Transparency Reports, provide data with large volumes of internet traffic because they are produced by companies offering internet services used by large populations across many countries around the world. Other data sources, such as NDT measurements, publish data collected from probes monitoring network performance globally. And other data sources, like Tor Metrics, publish daily measurements from around the world pertaining to the use of particular software.
Data sources that provide insight into the potential routing within networks can also be useful when examining internet blackouts. BGP data aggregated by RIPE, for example, enables us to monitor routing information for the country in question and to examine whether it has been disconnected from the internet.
In short, some public data sources that we refer to when examining internet blackouts include the following:
Google Product Traffic data (via Google Transparency Reports)
RIPE data (including public measurements and BGP announcement data)
Route Views Project BGP announcement data archive
Center for Applied Internet Data Analysis (CAIDA): Internet Outage Detection and Analysis (IODA)
Dyn Research: Outages Bulletin
Internet-Wide Scan Data Repository: Longterm DNS survey
The above list is not exhaustive and the listed data sources present various limitations. NDT measurement data, for example, is limited by the amount of probes deployed which may not include equal coverage across countries around the world, while Google traffic data is limited by the amount of users Google has in each country of question. Such data sources, however, can potentially help gain insight into the volume of internet traffic originating from various countries around the world through further examination.
Examining data sources
Most public data sources (as listed in the previous section) allow us to select a country and to observe the flow of internet traffic originating from it across time. If a country-wide internet blackout has occurred, we would expect to see almost no internet traffic origination from a country during the reported period of a blackout. This, for example, was evident in Ethiopia in August 2016, when an internet blackout was reported in the middle of political protests.
The graph below, taken from Google traffic data, clearly illustrates that no internet traffic was originating from Ethiopia between 6th to 8th August, confirming that an internet blackout occurred.
Similarly, Google traffic data in the graph below shows a clear disruption of internet traffic originating from the Gambia between 30th November to 2nd December, when an internet blackout was reported during the country’s 2016 presidential election.
In addition to Google traffic data, it’s also particularly useful to look at BGP announcement data aggregated by RIPE from different Remote Route Collectors (RRT) and to examine whether a country’s prefixes are withdrawn when an internet blackout is being reported. In the Gambia, for example, such data allowed us to monitor the withdrawal and announcement of Gambian prefixes during the country’s 2016 presidential election. In fact, Gambian prefixes were withdrawn between 30th November to 2nd December, which is consistent with the hypothesis that the internet blackout occurred during that period, as inferred through Google traffic data.
OONI has been working on a methodology to automatically identify and investigate cases of internet blackouts. So for this is something very experimental and will require more work to have it be production ready.
This ipython notebook (view) contains some of the results of the experiments we have been doing. The same methodology can easily be applied to other datasets other than the Google traffic data. It would have been useful, and much easier, if Google Transparency Reports provided an easy to use HTTP API for obtaining the data.
Cross-referencing data sources
Internet blackout might not always occur across all networks on a country-wide level. A government, for example, may order ISPs to shut down the internet only in a specific location where a protest is taking place, while keeping the internet accessible in the rest of the country. In such a case, it’s probably unlikely that Google traffic data would show a complete disruption as illustrated in the graphs of the previous section, but would rather show a general decrease in traffic data in comparison to previous dates. As such, cross-referencing data sources as part of an examination of internet blackout can be a useful next step.
One way of doing so is by looking at both Google traffic data and Tor Metrics for a particular country during the same period of time. It’s unlikely, for example, that there would be a total internet blackout if there is normal usage of the Tor network during a reported blackout. If, however, there is a spike in Tor usage and Google traffic data is significantly decreased in comparison to previous dates, then it might be the case that censorship events are occurring in certain networks, leading to the increased usage of censorship circumvention software.
This is illustrated through the example below where we can see decreased Google traffic data and increased usage of Tor software in Ethiopia in October 2016.
When cross-referencing data sources, it might also be useful to look at the geotagging of posts on social media platforms, such as Twitter. Unlike the aforementioned public data sources (e.g. Google transparency reports) which show an overview of traffic originating from a country, the geotags on posts allow us to drill down to a specific region or city in a country (if the geotags are accurate). This is particularly useful if, for example, locals are reporting a blackout in a country, Google traffic data does not show a clear blackout, but you’re interested in examining whether the approximate internet activity originating from a specific city or region has been disrupted.
This process involves:
Collecting all of the posts of a social media platform (e.g. via Twitter’s API) that are geotagged with the region that you are interested in;
Calculating the approximate amount of geotagged posts within the time range that you are interested in;
Evaluating whether there is a significant decrease of geotagged posts on the specific dates of a reported internet blackout.
This is illustrated through the graph below that we created based on tweets geotagged in Ethiopia between 3rd-9th August 2016, indicating a decrease of Twitter activity in the country on 7th August 2016 when an internet blackout was reported in the country.
However, such data should probably only be used supplementary when cross- referencing data sources, since it is subject to various limitations (for example, Twitter users in a country might be inactive due to reasons unrelated to an internet blackout).
Unless all data sources that you refer to show absolutely no internet traffic originating from a country or the prefixes within a country are completely withdrawn, it’s hard to reach an accurate conclusion on whether an internet blackout has occurred or not.
Nonetheless, the publicly available data sources mentioned in this post can help gain some insight into the flow of internet traffic in most countries around the world, and they can help provide signs of internet blackout.
We encourage more companies (such as Akamai, CloudFlare and Fastly) to publish data on network traffic originating from countries to help increase transparency when heavy network disruptions occur. We also encourage you all to support Access Now’s #KeepItOn campaign for a stable and open internet around the world.