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content/blog/2022-11-04-country-filtering.md
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content/blog/2022-11-04-country-filtering.md
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---
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layout: page
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title: "Crudely geo-filtering internet routes"
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description: "How I make routers drop traffic for certain countries"
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date: 2022-11-04
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tags: network
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draft: true
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extra:
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auto_center_images: true
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excerpt:
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---
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Sometimes I find the need to filter internet traffic traversing my networks based on country of origin. Commonly, I find that dropping certain countries (*cough Russia*) decreases the amount of scans and attacks I see to nearly zero. Sometimes its also nice to ignore routes that pass through countries with heavy surveillance policies too.
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In my network, I personally have two types of geographic filters:
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- Blacklist: Do not accept any traffic to or from a country
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- Depreference: Try to pick routes that do not pass through a country
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In order to implement any of this, I first needed a way to quantify which country originates a particular route being learned by an edge router. There are many ways to do this, generally following the rule that *more accuracy = much more effort*.
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While working on this system for myself, I came up with a few possible ways to programmatically determine a route's country of origin:
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- Parse the route's [geofeed](https://datatracker.ietf.org/doc/html/rfc9092) file
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- **Pro:** If the geofeed is implemented correctly, this is highly accurate
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- **Con:** This requires the prefix to have a covering geofeed in the first place
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- **Con:** Its really easy for a network to lie in their geofeed
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- Assume that [autonomous systems](https://en.wikipedia.org/wiki/Autonomous_system_(Internet)) operate in the country they are registered
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- **Pro:** Very easy to obtain the country of origin for an AS
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- **Con:** Global anycast exists
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- **Con:** Hobbynets like to lie about their country of operation
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- Infer geographical information from [BGP communities](https://www.rfc-editor.org/rfc/rfc1997)
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- **Pro:** If done right, this is very accurate
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- **Pro:** Sounds awesome, would be good talk material
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- **Con:** Documenting communities used by transit providers is a lot of work
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- **Con:** Not all transit providers use communities
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- **Con:** My upstreams like stripping communities from their routes sometimes
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After pondering my options, I ended up choosing to just filter on the country of origin attached to each AS number's records. This is very easy to obtain, and leaves room for me to do something fancy in the future now that I have the rest of the system in place too.
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## Getting a list of ASNs registered to a country
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If I were to update this filter regularly, I would try pulling [WHOIS](https://en.wikipedia.org/wiki/WHOIS) database dumps, parsing through the data, and creating a lookup table for this purpose.
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However, I really don't need to instantly catch networks as they are registered, and feel perfectly fine updating this filter every few months. Thus, the easy route.
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content/blog/2022-11-10-mega-playlists.md
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content/blog/2022-11-10-mega-playlists.md
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---
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layout: page
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title: "A brief walk through my all-time favorite songs"
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description: "..and crashing spotify clients in the process"
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date: 2022-11-10
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tags: music
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draft: true
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extra:
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auto_center_images: true
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excerpt:
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---
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Music is a core part of my daily life. For about 13 hours straight each day, I have all kinds of tracks playing while I work on things. Having a constant stream of music playing is a habit I have held for years, it keeps some rhythm in my day, and at the same time gives me plenty of chances to find new tracks and artists to listen to.
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My methods of listening to music have changed over the years a bit. As far as I can remember, I started listening to music on my own via YouTube sometime in elementary school. I could have the first two entries in the following list mixed up, but this should be the general order of how I have listened to songs over time:
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- YouTube
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- [Stingray Music](https://en.wikipedia.org/wiki/Stingray_Music)
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- iTunes
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- SoundCloud
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- YouTube ... again
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- Curated mixes via [Call of the Wild](https://www.monstercat.com/call-of-the-wild)
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- I listened to every single weekly release from episode **1** through roughly **285** and found many of my favorite artists here
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- ~~Youtube~~ [NewPipe](https://newpipe.net/)
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- Spotify
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It was sometime during my "NewPipe era" that I decided it might be a good idea to make a playlist of songs I liked, instead of just trying to remember and search them every time I wanted to listen to something I knew.
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It was with this decision that I spawned my project to track every one of my favorite songs over time.
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## The mega playlist
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Sometime in early 2020, after switching to use Spotify as my streaming service of choice, I started what I called the "mega playlist". This started by copying over my old NewPipe playlist, and reorganizing things a bit. During this time, I came up with a set of rules for this music collection:
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- Only the best of the best shall enter this playlist
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- Songs may be slightly out of order, but must generally fall in the order I listened to them
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- All songs must cleanly cross-fade with each other so I don't notice jarring switches when the track changes
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This last point came in to place because I was playing this mix while doing school work, and didn't like being interrupted by a sudden musical mood or tempo change.
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After a year or so of collecting songs for this project, the "mega playlist" was renamed to "Journey" (a journey through songs I like if you will), and I also [published it](https://open.spotify.com/playlist/3KoQ6Wqo6GexHLsRkEJB9u?si=ec9e54abfc70443d) on my Spotify page.
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I also realized it would be reasonably easy to extend the playlist *before* 2019 to include every song I remember liking. As it turns out, Spotify gets very unhappy if your playlists start pushing the 24 hour mark. Scrolling, searching, and moving songs will crash the mobile app at that point. So, I ended up creating a second pre-journey playlist, which I called [Intro](https://open.spotify.com/playlist/4DknNpYMzmfJdiwRWklJlK?si=675a811e35654090).
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## The highlights
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With the context out of the way, why not share some of the highlights of this project. As of the time of writing, I have a total of 38 hours of music in *Intro* and *Journey*.. Thats 646 songs! Last year I decided to test myself and listen to them all in order to see what I remembered. Surprisingly, I could hum the melody of every single one of them despite not listening to some for a few years. The power of a good song I guess :man_shrugging:
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### Where it *almost* all began
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Skipping some earlier rock music that I can't remember enough of to add to a playlist, the first electronic song I distinctly remember obsessing over was ***Top of the World*** by [Stephen Walking](https://open.spotify.com/artist/0FvgCHaMG7KL6M83yJuhmL).
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<iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/23MJSTyBjJBnthv83gmnEy?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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This track then led me to discover *[012: Aftermath](https://open.spotify.com/album/4iom89zyZI8s7ZgDmthiFx?si=LMnVVnC4QSa0usGLYuFczQ)*, probably my most listened to [Monstercat](https://www.monstercat.com/) album, which contained two songs that stuck in my head for years:
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<iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/7yW5kmVbaWVT3zKYFGcU2L?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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<br>
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<iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/2XsbGteXQCFYERLww9cwq7?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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Another notable mention is one of the weirdest music videos I have ever seen, which was produced for Stephen Walking's [Short Shorts](https://www.youtube.com/watch?v=-JNDliFODF4).
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### Exploring the electronic sub-genres
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After listening to a fair bit of dubstep, and dubstep-ish tracks, I eventually came to find some lighter tracks. My favorite of which was *Turbo Penguin*
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<iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/3cKpKxz7JOZsCf4C2rRB5K?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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I also discovered an artist by the name of [San Holo](https://en.wikipedia.org/wiki/San_Holo), who pioneered the [Future Bass](https://en.wikipedia.org/wiki/Future_bass) genre which I came to love. I played his *Victory* EP on repeat for a long time.
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<iframe src="https://open.spotify.com/embed/album/4RxpFCkTTBNALA3njzCe4Y?utm_source=generator" width="100%" height="180" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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Continuing on the trend of finding artists I liked, ,I also discovered [Tokyo Machine](https://open.spotify.com/artist/3bwENxqj9nhaAI3fsAwmv9?si=04zUDzfXTgyHsK5J1ZqkXg) and was quite fascinated by his take on chiptune with a bass house twist.
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### Going heavy again
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I have found that no matter the genres and artists I find myself listening to, my general musical mood flip-flops between bouncy, light tracks, and excessively heavy, bassy tracks.
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Keeping with the semi-chronology of this post, I went on to listen to heavier, more rave-fit tracks like *Gorilla Glue* by [Dirty Audio](https://open.spotify.com/artist/1WGex9YdmT4TZjmmMirZA8), *BAMF* by the [Pegboard Nerds](https://en.wikipedia.org/wiki/Pegboard_Nerds), *Panic* by [Dion Timmer](https://open.spotify.com/artist/06VibSJEr3GLxLBBZhRums) and *many many more* (tracks 104 to 187 of [Intro](https://open.spotify.com/playlist/4DknNpYMzmfJdiwRWklJlK?si=675a811e35654090)).
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<iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/1VyXn3bJyGczirLLCAywmD?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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<br>
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<iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/73nWSc3002fGRPrnFDev8x?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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<br>
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<iframe style="border-radius:12px" src="https://open.spotify.com/embed/track/68RuKZzcIp8X3p7PgG6mbU?utm_source=generator" width="100%" height="80" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"></iframe>
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A brief intermission of vocal-filled melodic tracks was induced after this period due to the release of [Instinct](https://open.spotify.com/album/7qzURREdjo1w2mKBGGcDkZ?si=4hKGb9YAR1iFP3UBWzNsDg), another great Monstercat album.
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@ -13,7 +13,8 @@ Here is a list of social platforms I am reasonably active on:
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- [LinkedIn](https://www.linkedin.com/in/ewpratten/)
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- [GitHub](https://github.com/ewpratten)
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- [Instagram](https://www.instagram.com/evanpratten/)
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- [Twitter](https://twitter.com/ewpratten/)
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<!-- - [Twitter](https://twitter.com/ewpratten/) -->
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- [Mastodon](https://ewp.fyi/mastodon)
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- [QRZ](https://qrz.com/db/va3zza)
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- [Spotify](https://open.spotify.com/artist/1aLNEmgqBJkhfkEZvf8Vh5)
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- [Apple Music](https://music.apple.com/us/artist/evan-pratten/1611566708)
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6
content/logbook/2022-november.md
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content/logbook/2022-november.md
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---
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title: November 2022
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---
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- **Nov 4**
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- Early afternoon: QSO with VA3ZEV (Dave) via the VE3RSB repeater
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content/notes/_index.md
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content/notes/_index.md
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---
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title: Notes
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extra:
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inject_notes: true
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---
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# Notes
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A list of smaller notes I have taken on things. Can be thought of as future blog posts.
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content/notes/bgp-country-blocks.md
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content/notes/bgp-country-blocks.md
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---
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title: Blocking and depreferring BGP routes from specific countries
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---
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Hurricane Electric keeps a somewhat up-to-date list of ASNs registered to each country in the world. This is obtainable via:
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```sh
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export COUNTRY=CA # This is the country code to look up
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curl -A "asd" https://bgp.he.net/country/$COUNTRY | grep -o -E "AS([0-9]+)" | sort | uniq
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# You should get a list of ASNs piped to stdout
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```
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Two types of filters are handy. Blackholing, and depreferring.
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})(window, document, 'script', 'dataLayer', 'GTM-KFJ95RP');</script>
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<!-- End Google Tag Manager -->
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<meta charset="UTF-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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@ -89,6 +91,7 @@
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<p>
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<a href="/">Home</a> |
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<a href="/blog">Blog</a> |
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{# <a href="/blog">Rabbit Hole</a> | #}
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{# <a href="/work">My Work</a> | #}
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{# <a href="/network">Network</a> | #}
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<a href="/contact">Contact</a>
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@ -154,6 +157,9 @@
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window.location.href = window.location.href.replace('retrylife.ca', 'ewpratten.com');
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}
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</script>
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{# Meta magic for mastodon verification #}
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<a rel="me" href="https://social.ewpratten.com/@evan" style="display:none;"></a>
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</body>
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</html>
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{{ super() }}
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{# Disable crawling if requested #}
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{% if page.extra.no_crawl %}
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<meta name="robots" content="noindex, nofollow">
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{% endif %}
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{% if page.extra.uses_katex %}
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<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-MML-AM_CHTML' async></script>
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<script type="text/x-mathjax-config">
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{% block head %}
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{{ super() }}
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{# Disable crawling if requested #}
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{% if section.extra.no_crawl %}
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<meta name="robots" content="noindex, nofollow">
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{% endif %}
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<meta property="og:title" content="{{section.title}} - Evan Pratten" />
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{% if section.description %}
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<meta property="og:description" content="{{section.description}}" />
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@ -91,4 +97,17 @@
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{% endfor %}
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</ul>
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{% endif %}
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{# Notes #}
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{% if section.extra.inject_notes %}
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<ul>
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{% for page in section.pages %}
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{% if not page.extra.hidden %}
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<li class="blog-post-li"><a href="{{page.path}}">{{page.title}}</a>
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</li>
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{% endif %}
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{% endfor %}
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</ul>
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{% endif %}
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{% endblock content %}
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