|
|
|
|
|
by vscode-rest
59 days ago
|
|
Something like Lomb–Scargle would possibly be a better fit I suppose. But yes that sort of flow, I could do it as a one off with a Python script as you state, but my interest is more if anyone has sunk their teeth into network packet analysis in the frequency domain from the ground up and wrapped up all the learnings into a thoughtfully designed interface. I was searching for a Wireshark type plugin to do this but I couldn’t find anything. Alternatively, equally useful would be learning about anyone who has started to do something like this and then realized that it didn’t actually help them analyze anything. |
|
I've tried to use Lomb-Scargle to reduce the number of sampling points in magnetic resonance experiments, but had another dimension to take into account (similar to doing the analysis for each network port separately in your case). I got some spikes on some of the 'ports' which I couldn't reason about or reproduce when I did the same with periodic sampling and FFT. But the individual periodograms looked reasonable, if I remember correctly. Maybe we have a more regular user of LS around, who can point out common pitfalls. Otherwise you could generate some data from known frequencies to see what kind of artifacts you get.
You could maybe also take a look at the auto-correlation of the packet timestamps to see whether you can extract timescales on which patterns arise.
[1]: https://iopscience.iop.org/article/10.3847/1538-4365/aab766