https://github.com/s0md3v/Orbit
Blockchain Transactions Investigation Tool
https://github.com/s0md3v/Orbit
bitcoin blockchain osint
Last synced: 22 days ago
JSON representation
Blockchain Transactions Investigation Tool
- Host: GitHub
- URL: https://github.com/s0md3v/Orbit
- Owner: s0md3v
- License: gpl-3.0
- Created: 2018-07-10T14:38:34.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-07-15T09:47:59.000Z (over 2 years ago)
- Last Synced: 2025-03-21T22:05:49.748Z (24 days ago)
- Topics: bitcoin, blockchain, osint
- Language: Python
- Homepage:
- Size: 56.6 KB
- Stars: 555
- Watchers: 33
- Forks: 159
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
ＯＲＢＩＴ
Blockchain Transactions Investigation Tool
### Introduction
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:** Orbit only runs on Python 3.2 and above.
### Usage
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 `-l` option.
```
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 `-d` option.
```
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 `-t` option.
```
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
```
Support Formats
- `graphml` (Supported by most graph viewers)
- `json` (For raw processing)
This is your terminal dashboard.
### Visualization
Once the scan is complete, the graph will automatically open in your default browser. If it doesn't open, open `quark.html` manually.
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.
As Orbit uses  to render the graph, more information about the various features and controls is available in Quark's README.