Cyber Threat Nexus Knowledge Graph
Description
The Cyber Threat Nexus Knowledge Graph is a tool designed to revolutionize vulnerability management by integrating and synthesizing diverse data sources into a unified framework. Built upon the foundational research presented in "A Relevance Model for Threat-Centric Ranking of Cybersecurity Vulnerabilities" by Corren McCoy, Ross Gore, Michael L. Nelson, and Michele C. Weigle, this knowledge graph empowers organizations to prioritize vulnerabilities based on real-world threat intelligence and adversary behaviors. Purpose The relentless challenge of tracking and remediating cybersecurity vulnerabilities requires a strategic approach to ensure organizational defenses remain robust. The The Cyber Threat Nexus Knowledge Graph addresses this need by aggregating public data sources, such as the Common Vulnerability Scoring System (CVSS), MITRE ATT&CK adversary tactics, and software vulnerability datasets, to provide personalized, automated recommendations for vulnerability prioritization. Key Features Threat-Centric Ranking: Focuses on vulnerabilities most likely to be exploited by cyber threat actors, enabling organizations to concentrate remediation efforts where they matter most. Data Integration: Links large datasets from public and proprietary sources into a cohesive graph structure, facilitating semantic queries and advanced analytics. Adversary Criteria: Incorporates adversary tactics and techniques derived from MITRE ATT&CK to align vulnerability management with real-world attack scenarios. Performance Metrics: Utilizes Normalized Discounted Cumulative Gain (nDCG) to measure ranking policy effectiveness, demonstrating significant improvements over traditional CVSS-based approaches. Cost Optimization: Provides actionable insights that reduce annualized remediation costs by up to 25.5%, delivering measurable return on investment (ROI). Impact The The Cyber Threat Nexus Knowledge Graph enhances cybersecurity resilience by enabling organizations to: Identify vulnerabilities most likely to be exploited. Develop tailored remediation strategies aligned with organizational objectives. Improve efficiency in patching workflows while reducing costs. Leverage semantic queries for flexible, data-driven decision-making.
Files
Steps to reproduce
Neo4j Database Loading Guide This guide provides instructions for loading a Neo4j database from JSON files and database dump files. It covers both methods and highlights key considerations for efficient data import. Table of Contents Prerequisites Loading JSON Data Using APOC Library Bulk Import with apoc.import.json Loading Database Dumps Offline Loading via neo4j-admin Docker Container Loading Neo4j Desktop Import Key Considerations Prerequisites Before proceeding, ensure the following: Neo4j is installed (Community or Enterprise Edition). The APOC library is installed for JSON imports (APOC Docs). You have access to the JSON files or .dump files to be imported. Sufficient disk space is available for the database. Loading JSON Data 1. Using APOC Library The APOC library provides powerful tools for importing JSON data into Neo4j. Steps: Place your JSON file in the import directory of your Neo4j installation. Run the following Cypher query to load data: text CALL apoc.load.json('file:///data.json') YIELD value UNWIND value.nodes AS node CALL apoc.create.node(node.labels, node.properties) YIELD node WITH value UNWIND value.relationships AS rel MATCH (a), (b) WHERE a.id = rel.startNode AND b.id = rel.endNode CALL apoc.create.relationship(a, rel.type, rel.properties, b) YIELD rel RETURN count(*) Use MERGE instead of CREATE if you need to avoid duplicates. Tips: For large datasets, use batch processing with apoc.periodic.iterate: text CALL apoc.periodic.iterate( 'CALL apoc.load.json("file:///data.json") YIELD value RETURN value', 'UNWIND value.nodes AS node CALL apoc.create.node(node.labels, node.properties) YIELD node RETURN count(*)', {batchSize: 1000} ) 2. Bulk Import with apoc.import.json If your JSON file is pre-formatted (e.g., exported via apoc.export.json), you can use apoc.import.json. Steps: Place the JSON file in the import directory. Run the following Cypher query: text CALL apoc.import.json('file:///exported_data.json', { batchSize: 10000, unwindBatchSize: 100 }) Advantages: Handles up to 10k nodes/relationships per second. Supports cloud storage URLs (e.g., S3, GCS, Azure Blob). Loading Database Dumps 1. Offline Loading via neo4j-admin For restoring a full database from a .dump file using the Neo4j Admin CLI: Steps: Stop the Neo4j service: bash neo4j stop Load the dump file: bash neo4j-admin database load neo4j --from-path=/path/to/backups --overwrite-destination=true Restart the Neo4j service: bash neo4j start Notes: This method requires Enterprise Edition for online loading. Community Edition must perform this operation offline.
Institutions
- Old Dominion University