Uses Sharphound, Bloodhound and Neo4j to produce an actionable list of attack paths for targeted remediation.
-
Updated
Jul 9, 2024 - Python
Uses Sharphound, Bloodhound and Neo4j to produce an actionable list of attack paths for targeted remediation.
Working through "The Movie Graph" as Py2Neo
Here we will sort out a variety of interesting Python library learning
Graph Representation of MITRE ATT&CK's CTI data
An exploratory, tutorial and analytical view of the Unified Medical Language System (UMLS) & the software/technologies provided via being a free UMLS license holder. This repo will subset 2021AB UMLS native release, introduce/build upon UMLS provided tools to load a configured subset into first a relational database --> MySQL, SQLite, PostgreSQL…
Develop a personalized recommendation system using a Knowledge Graph to model relationships between users, products, and interactions. Utilizing Python, Neo4j, Cypher, and Py2neo, this project aims to enhance user satisfaction through efficient data management and advanced recommendation algorithms.
A search and recommender system based on Elasticsearch, Neo4j, Flask, Apache
Some scripts/guides for working with Neo4j in Python.
Cypher access to Neo4J via IPython
A tool to import SnpEff annotated files to a Neo4j Graph database
Analyze contributors to PyPi using Libraries.io data
COMBAT-TB model is a Chado inspired graph model for genome annotation.
Add a description, image, and links to the py2neo topic page so that developers can more easily learn about it.
To associate your repository with the py2neo topic, visit your repo's landing page and select "manage topics."