A modular framework for forensic investigators, presented by Iasonas Polakis of Columbia University, designed to collect and analyze user data from online social networks and digital communication services. The system extracts data from user profiles using stored credentials and session cookies, then correlates profiles across different services to map them to the same individual. A case study demonstrated the system's effectiveness, finding significant coverage of users across services through automated correlation.
Use Cases
- Mapping user identities across multiple online services based on the described correlation techniques.
- Analyzing user activities and interactions from social network data using the framework's visualization component.
- Collecting digital evidence from social footprints, including messages and location information, as described for forensic procedures.
Strengths
- Framework includes automated correlation process achieving significant user coverage across services, as per the case study.
- System is modular, handling data collection, correlation, and visualization specifically for online social network data.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Iasonas Polakis, Columbia University
- Collection Method
- Data collection modules extract data from user social network profiles and communication services using stored credentials and session cookies.