dfTimewolf
A framework for orchestrating forensic collection, processing and data export
| Category | Incident Response & Forensics |
|---|---|
| Community Stars | 302 |
| Last Commit | last week |
| Last page update | 19 days ago |
| Pricing Details | Free and open-source |
| Target Audience | Forensic analysts, Incident responders, Security professionals |
dfTimewolf addresses the complex challenge of orchestrating forensic collection, processing, and data export in cloud and on-premise environments by providing a modular and recipe-driven framework. At its core, dfTimewolf consists of collectors, processors, and exporters that are chained together through predefined "recipes," which are essentially instructions on how to launch and chain these modules.
The technical architecture of dfTimewolf is built around these modules, each with specific roles: collectors gather data from various sources such as AWS, GCP, and Azure; processors analyze and transform the collected data using tools like plaso and Timesketch; and exporters handle the output, whether it's logging to a filesystem, uploading to cloud storage, or integrating with other forensic tools like GRR and Turbinia. Recipes define the sequence and configuration of these modules, allowing for flexible and customized workflows.
Operationally, dfTimewolf requires careful management of dependencies and configurations. It must be installed in a virtual environment, and additional modules like log2timeline need to be installed separately and made available. The tool uses a configuration file (typically config.json) to load settings, and logging is directed to both stdout and a log file. Each recipe has its own set of optional and positional arguments, which can be detailed using the -h flag, ensuring that users can tailor the execution to their specific needs.
Key technical details include the use of Poetry for dependency management, support for multiple cloud providers (AWS, GCP, Azure), and integration with forensic tools like Timesketch and GRR. However, the complexity of the recipes and the need for precise configuration can introduce operational overhead, particularly in large-scale deployments where managing multiple modules and dependencies can become cumbersome. Additionally, the performance and resource usage of dfTimewolf can vary significantly depending on the specific recipes and the volume of data being processed.