Visual Analysis

Visual Analysis

What is Visual Analysis?

• Graph Analysis: Also known as visual or link analysis, it involves the visual representation of nodes (entities) and edges (connections) in a graph.

• Purpose: Helps analyze and visualize threat trends by showing relationships and patterns in data.

Why is Visual Analysis Important?

1. Faster Information Processing: The human brain processes visual information much quicker than written data, leading to faster comprehension and action.

2. Insight Discovery: Interacting with visual data helps uncover insights that might be missed in traditional data formats.

3. Pattern Recognition: Visualizing data helps identify patterns and context, making it easier to understand complex relationships.

4. Accessibility: Graph visualization tools can be used by non-technical users, making insights accessible to a broader audience.

Benefits of Graph Technology

• Combines Multidimensional Data: Aggregates data from multiple sources into a single comprehensive model.

• Scalability: Can handle large amounts of data, scaling up to billions of nodes and edges.

• Suspicious Activity Detection: More easily identifies suspicious patterns and anomalies by analyzing the dynamics between entities.

Tools for Visual Analysis

1. KeyLines: A JavaScript SDK for interactive graph visualization that works with various data sources and formats.

2. Linkurious: Allows analysts to visually investigate large data collections, search for patterns, and reduce noise through data filters and visual styles.

3. Maltego: A widely used data mining tool in cybersecurity that creates directed graphs for analysis. The Community Edition is available for free with some limitations.

Practical Application

• Graph Visualization: Useful for visualizing relationships and understanding the context of data.

• Combining Data: Aggregates data from different sources to identify suspicious patterns and behaviors.

• User-Friendly: Tools like Maltego and Linkurious make it easier for users without programming skills to interact with and analyze data.