Orbit is designed to explore network of a blockchain wallet by recursively crawling through transaction history. The data is rendered as a graph to reveal major sources, sinks and suspicious connections.
Note: It only runs on Python 3.2 and above.
Let’s start by crawling transaction history of a wallet
python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F
Crawling multiple wallets is no different.
python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F,1ETBbsHPvbydW7hGWXXKXZ3pxVh3VFoMaX
Orbit fetches last 50 transactions from each wallet by default, but it can be tuned with
python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -l 100
Orbit’s default crawling depth is 3 i.e. it fetches the history of target wallet(s), crawls the newly found wallets and then crawls the wallets in the result again. The crawling depth can be increased or decresead with
python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -d 2
Wallets that have made just a couple of interactions with our target may not be important, Orbit can be told to crawl top N wallets at each level by using the
python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -t 20
If you want to view the collected data with a graph viewer of your choice, you can use -o option.
python3 orbit.py -s 1AJbsFZ64EpEfS5UAjAfcUG8pH8Jn3rn1F -o output.graphml
graphml(Supported by most graph viewers)
json(For raw processing)
This is your terminal dashboard.
Once the scan is complete, the graph will automatically open in your default browser. If it doesn’t open, open
quark.htmlmanually. Don’t worry if your graph looks messy like the one below or worse.
Select the Make Clusters option to form clusters using community detection algorithm. After that, you can use Color Clusters to give different colors to each community and then use Spacify option to fix overlapping nodes & edges.
The thickness of edges depends on the frequency of transactions between two wallets while the size of a node depends on both transaction frequency and the number of connections of the node.