Import, visualize, and analyze SpiderFoot OSINT data in Neo4j, a graph database

Overview

SpiderFoot Neo4j Tools

Import, visualize, and analyze SpiderFoot OSINT data in Neo4j, a graph database

A big graph

Step 1: Installation

NOTE: This installs the sfgraph command-line utility

$ pip install spiderfoot-neo4j

Step 2: Start Neo4j

NOTE: Docker must first be installed

$ docker run --rm --name sfgraph -v "$(pwd)/neo4j_database:/data" -e 'NEO4J_AUTH=neo4j/CHANGETHISIFYOURENOTZUCK' -e 'NEO4JLABS_PLUGINS=["apoc", "graph-data-science"]' -e 'NEO4J_dbms_security_procedures_unrestricted=apoc.*,gds.*' -p "7474:7474" -p "7687:7687" neo4j

Step 3: Import Scans

Spiderfoot scan ID in web browser

$ sfgraph path_to/spiderfoot.db -s   ...

Step 4: Browse Spiderfoot Data in Neo4j

Visit http://127.0.0.1:7474 and log in with neo4j/CHANGETHISIFYOURENOTZUCK Spiderfoot data in Neo4j

Step 5 (Optional): Use cool algorithms to find new targets

The --suggest option will rank nodes based on their connectedness in the graph. This is perfect for finding closely-related affiliates (child companies, etc.) to scan and add to the graph. By default, Harmonic Centrality is used, but others such as PageRank can be specified with --closeness-algorithm

$ sfgraph --suggest DOMAIN_NAME

Closeness scores

Example CYPHER Queries

() RETURN p # shortest path to all INTERNET_NAMEs from seed domain MATCH p=shortestPath((d:DOMAIN_NAME {data:"evilcorp.com"})-[*]-(n:INTERNET_NAME)) RETURN p # match only primary targets (non-affiliates) MATCH (n {scanned: true}) return n # match only affiliates MATCH (n {affiliate: true}) return n ">
# match all INTERNET_NAMEs
MATCH (n:INTERNET_NAME) RETURN n

# match multiple event types
MATCH (n) WHERE n:INTERNET_NAME OR n:DOMAIN_NAME OR n:EMAILADDR RETURN n

# match by attribute
MATCH (n {data: "evilcorp.com"}) RETURN n

# match by spiderfoot module (relationship)
MATCH p=()-[r:WHOIS]->() RETURN p

# shortest path to all INTERNET_NAMEs from seed domain
MATCH p=shortestPath((d:DOMAIN_NAME {data:"evilcorp.com"})-[*]-(n:INTERNET_NAME)) RETURN p

# match only primary targets (non-affiliates)
MATCH (n {scanned: true}) return n

# match only affiliates
MATCH (n {affiliate: true}) return n

CLI Help

sfgraph [-h] [-db SQLITEDB] [-s SCANS [SCANS ...]] [--uri URI] [-u USERNAME] [-p PASSWORD] [--clear] [--suggest SUGGEST]
               [--closeness-algorithm {pageRank,articleRank,closenessCentrality,harmonicCentrality,betweennessCentrality,eigenvectorCentrality}] [-v]

optional arguments:
  -h, --help            show this help message and exit
  -db SQLITEDB, --sqlitedb SQLITEDB
                        Spiderfoot sqlite database
  -s SCANS [SCANS ...], --scans SCANS [SCANS ...]
                        scan IDs to import
  --uri URI             Neo4j database URI (default: bolt://127.0.0.1:7687)
  -u USERNAME, --username USERNAME
                        Neo4j username (default: neo4j)
  -p PASSWORD, --password PASSWORD
                        Neo4j password
  --clear               Wipe the Neo4j database
  --suggest SUGGEST     Suggest targets of this type (e.g. DOMAIN_NAME) based on their connectedness in the graph
  --closeness-algorithm {pageRank,articleRank,closenessCentrality,harmonicCentrality,betweennessCentrality,eigenvectorCentrality}
                        Algorithm to use when suggesting targets
  -v, -d, --debug       Verbose / debug
Owner
Black Lantern Security
Security Organization
Black Lantern Security
Peloton Stats to Google Sheets with Data Visualization through Seaborn and Plotly

Peloton Stats to Google Sheets with Data Visualization through Seaborn and Plotly Problem: 2 peloton users were looking for a way to track their metri

9 Jul 22, 2022
By default, networkx has problems with drawing self-loops in graphs.

By default, networkx has problems with drawing self-loops in graphs. It makes it hard to draw a graph with self-loops or to make a nicely looking chord diagram. This repository provides some code to

Vladimir Shitov 5 Jan 06, 2022
Bar Chart of the number of Senators from each party who are up for election in the next three General Elections

Congress-Analysis Bar Chart of the number of Senators from each party who are up for election in the next three General Elections This bar chart shows

11 Oct 26, 2021
FURY - A software library for scientific visualization in Python

Free Unified Rendering in Python A software library for scientific visualization in Python. General Information • Key Features • Installation • How to

169 Dec 21, 2022
Simple function to plot multiple barplots in the same figure.

Simple function to plot multiple barplots in the same figure. Supports padding and custom color.

Matthias Jakobs 2 Feb 21, 2022
Python histogram library - histograms as updateable, fully semantic objects with visualization tools. [P]ython [HYST]ograms.

physt P(i/y)thon h(i/y)stograms. Inspired (and based on) numpy.histogram, but designed for humans(TM) on steroids(TM). The goal is to unify different

Jan Pipek 120 Dec 08, 2022
1900-2016 Olympic Data Analysis in Python by plotting different graphs

🔥 Olympics Data Analysis 🔥 In Data Science field, there is a big topic before creating a model for future prediction is Data Analysis. We can find o

Sayan Roy 1 Feb 06, 2022
Implement the Perspective open source code in preparation for data visualization

Task Overview | Installation Instructions | Link to Module 2 Introduction Experience Technology at JP Morgan Chase Try out what real work is like in t

Abdulazeez Jimoh 1 Jan 23, 2022
Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver

Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver, the wheel size, gear shifting sequence by modeling drivetrain constrai

Sabbella Prasanna 1 Jan 11, 2022
This project is created to visualize the system statistics such as memory usage, CPU usage, memory accessible by process and much more using Kibana Dashboard with Elasticsearch.

System Stats Visualizer This project is created to visualize the system statistics such as memory usage, CPU usage, memory accessible by process and m

Vishal Teotia 5 Feb 06, 2022
Plotly Dash Command Line Tools - Easily create and deploy Plotly Dash projects from templates

🛠️ dash-tools - Create and Deploy Plotly Dash Apps from Command Line | | | | | Create a templated multi-page Plotly Dash app with CLI in less than 7

Andrew Hossack 50 Dec 30, 2022
Calendar heatmaps from Pandas time series data

Note: See MarvinT/calmap for the maintained version of the project. That is also the version that gets published to PyPI and it has received several f

Martijn Vermaat 195 Dec 22, 2022
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

AutoViz Automatically Visualize any dataset, any size with a single line of code. AutoViz performs automatic visualization of any dataset with one lin

AutoViz and Auto_ViML 1k Jan 02, 2023
This package creates clean and beautiful matplotlib plots that work on light and dark backgrounds

This package creates clean and beautiful matplotlib plots that work on light and dark backgrounds. Inspired by the work of Edward Tufte.

Nico Schlömer 205 Jan 07, 2023
Ana's Portfolio

Ana's Portfolio ✌️ Welcome to my Portfolio! You will find here different Projects I have worked on (from scratch) 💪 Projects 💻 1️⃣ Hangman game (Mad

Ana Katherine Cortes Sobrino 9 Mar 15, 2022
CPG represent!

CoolPandasGroup CPG represent! Arianna Brandon Enne Luan Tracie Project requirements: use Pandas to clean and format datasets use Jupyter Notebook to

Enne 3 Feb 07, 2022
Simple, realtime visualization of neural network training performance.

pastalog Simple, realtime visualization server for training neural networks. Use with Lasagne, Keras, Tensorflow, Torch, Theano, and basically everyth

Rewon Child 416 Dec 29, 2022
Graphical visualizer for spectralyze by Lauchmelder23

spectralyze visualizer Graphical visualizer for spectralyze by Lauchmelder23 Install Install matplotlib and ffmpeg. Put ffmpeg.exe in same folder as v

Matthew 1 Dec 21, 2021
Open-questions - Open questions for Bellingcat technical contributors

Open questions for Bellingcat technical contributors These are difficult, long-term projects that would contribute to open source investigations at Be

Bellingcat 234 Dec 31, 2022
IPython/Jupyter notebook module for Vega and Vega-Lite

IPython Vega IPython/Jupyter notebook module for Vega 5, and Vega-Lite 4. Notebooks with embedded visualizations can be viewed on GitHub and nbviewer.

Vega 335 Nov 29, 2022