Cyber Sleuth: Finding Hidden Connections in Cyber Data

Defenders think in lists, but attackers think in graphs. Learn how to map your network's hidden connections and find critical vulnerabilities before they do.

#1about 6 minutes

Understanding the complexity of modern cybersecurity threats

Cybersecurity involves diverse domains and multi-step attack chains that require compiling data from multiple sources to prevent breaches.

#2about 6 minutes

Why attackers think in graphs, not lists

Attackers exploit interconnected pathways to hop between systems, while defenders often focus on static lists of permissions, creating a strategic disadvantage.

#3about 6 minutes

Modeling connected data with graph databases

Graph databases use nodes, relationships, and labels to create a holistic view of a network, making it easier to surface hidden connections.

#4about 6 minutes

Getting started with Neo4j and the Cypher query language

Learn how to load data into Neo4j using dump files or the APOC library and write basic `CREATE` and `MATCH` queries with the Cypher language.

#5about 7 minutes

Investigating a user's direct and indirect access

A live demo shows how to use Cypher to trace a user's access from direct machine permissions to multi-hop group memberships.

#6about 5 minutes

Visualizing threats with rule-based styling in Bloom

Use Neo4j Bloom to visually explore the graph with natural language queries and apply rule-based styling with algorithms like PageRank to highlight critical assets.

#7about 5 minutes

Analyzing the blast radius of a compromised account

Discover how to find the shortest attack path to a high-value target and visualize the full blast radius of a single compromised account.

#8about 3 minutes

Resources for learning more about Neo4j

Find resources to continue learning, including a GitHub repository with the dataset, Neo4j sandboxes, and the Graph Academy.

#9about 16 minutes

Q&A on graph databases for cybersecurity

The speaker answers audience questions about required skills, tracking malware, and integrating machine learning with graph databases for threat detection.