Reviews and Comments

This is required reading for every new Thinkst employee, and it was a treat to be exposed to it. It helps contextualize the process of getting stuff done, and how easy it is to build processes and offramps to not focusing on what is important.

Coming back to it periodically when I've had a bit of a lull in my own research helps to revive my interest in exploring and learning new things through research.

I've been enjoying Nim a lot, but finding gaps in my understanding/knowledge, hopefully this will smooth those out so I can target a larger scale project without constant Googling.

[Included in ThinkstScapes]

This paper explored using ML techniques to identify LTE devices streaming specific content via their bandwidth fingerprint. The authors identify that video streaming encodes a specific duration of video into a data-chunk, so each video has a unique sequence of transmitted chunk sizes, allowing for fingerprinting a media sample, and then classifying an encrypted network stream to determine if it is that video.

The experiment ran both open and closed world, and showed high accuracy, even with other device processes using data, and with other channel usage to increase channel capacity. In short, they were able to [with high confidence] determine what video every LTE device was watching in a cell (assuming it was seen prior).