Task 1 First you will need to find public information about an event that had an impact on Internet connectivity for individual AS or entire countries. Example types of events are DDoS attacks, prefix hijacking, political developments, social unrest, social media censorship, an earthquake, or other physical phenomena. A specific example event is the development that took place in Myanmar and it was accompanied with Internet disruptions after midnight (UTC) on 1st February 2021. Here is a related news article. We will be using this event as an illustrated example in the project description, but please identify different events to work with. Where to find events Below we list resources that can help you find information about similar events. Of course, you are more than welcome to expand your search on the news or on additional tools and resources. Ioda Internet Outage Detection and Analysis provides multiple events, and a feed as well. We can use the Ioda system to find information specifically for the Myanmar event and impacted ASes MPT (AS9988) and Mytel (AS136255) as shown here. Oracle Internet Intelligence. Using the Oracle Internet Intelligence to learn about the Myanmar event as shown here. Google traffic disruptions We can leverage the Google traffic disruptions to find out more about the Myanmar event as shown here. Netblocks.org provides reports of disruption events. Task 1 deliverables 1. Describe in a short essay of 10-15 sentences the event that took place. 2. Identify the time period the event took place. 3. Identify the AS numbers (and associated entities or organization names) of the networks that were involved/impacted.

4. Identify a metric that is associated with the control plane behavior of one impacted AS. Briefly justify why your metric is relevant. Task 2 Study the control plane behavior of the impacted AS before, during and after the event took place. Task 2 deliverables 1) Use the PyBGPstream library to study/track this metric. Example metrics are the number of prefixes that are advertised by an origin AS, the duration between Announcement and Withdrawal for a prefix, the AS path and any changes it is associated with, change of origin AS for a prefix, advertisements with conflicting multiple origin AS for a single prefix, etc. Of course, feel free to come up with your own metric that better reflects the behavior of the AS you are studying. We will need to be able to run your code and reproduce your result. Submitting hardcoded values (instead of code that pulls data, processes it, and produces your metric values) is not acceptable and will result in a 0 for the entire project. If you need a code snippet example to get started, the below example code snippet shows how to grab data for a specific time period. https://bgpstream.caida.org/docs/tutorials/pybgpstream#moas