Lateral Vision is an advanced tool designed for the detection and visualization of lateral movement in Windows domains. It integrates graph analysis, anomaly detection, and directed graphs, offering a comprehensive solution for today's sophisticated cyberattacks.
- Graph Analysis & Anomaly Detection: Utilizes advanced techniques to analyze network activities and detect anomalies.
- Directed Graph Visualization: Employs D3.js for creating clear, scalable, and interactive visualizations of network entities.
- Customizable User Interface: Tailored to meet the specific needs of cybersecurity professionals, enhancing data accessibility and usability.
- Efficient Data Processing Pipeline: Manages various data formats, enhancing the tool’s adaptability and maintainability.
Lateral Vision's architecture comprises:
- Main Program Processor: Processes and prepares log data for analysis.
- Neo4j Database: Efficient in handling graph-based data structures, ideal for network analysis.
- API Integration: Facilitates data upload, querying, and visualization.
- D3.js Visualizations: Offers dynamic and interactive graphs for visualizing lateral movement patterns.
- Python: Chosen for its extensive library ecosystem and readability.
- Neo4j and Flask: Facilitate graph database management and web application development.
- Directed Graphs: Effectively represent network relationships and interactions.
- Interactivity & Customization: Features like zoom, pan, and customizable visual elements enhance user engagement.
- Conducted in a controlled virtual environment to simulate real-world network complexities.
Enhancements include:
- Integrating diverse data collection modules.
- Optimizing anomaly detection algorithms.
- Implementing advanced visualization techniques.
- Conducting user studies for UI improvement.
- Expanding support for various event log types.