Last week I received an email in my inbox about a hackathon hosted by the awesome folks over at Amberdata. They are a provider for on-chain data and cover a large variety of blockchains – including Ethereum, Bitcoin, and Stellar. I have met the developers in the team in late 2018 when I was looking […]
I recently found myself in the situation where I was given access to a huge MySQL database that contained network traffic flows and IDS signature match data. As I work a lot with graph-based approaches, I needed to convert the table’s flow data into a graphml file for later visualization and analysis with scripts I have already written. Now without further ado here’s the code…
A few days back I stumbled across an interesting problem. I was asked to develop a solution that was doing some analysis work on geolocation data stored in KMZ format. Existing solutions like fastkml (64KB) and pykml (42KB) seemed nice at the first glance, proved to be unnecessary overhead, however. They’re mostly meant to manipulate and write data into KML format. I just needed to read the data for my later calculations. So I decided to build a solution using the Python Standard Library.
For some research on botnet host detection in large-scale networks, I found myself in the situation that I had to apply a set of algorithms to a huge packet dump. To comprehend an amazing paper, I started to play around with the dataset and tried to reproduce the results presented in the whitepaper. Quickly I realized that there was something fishy with my own dataset, so I fired up jupyter-notebook to gain some more insight in the IP structure of my dataset.