An asynchronous forensic data presentation framework for incident response, built on Elasticsearch.
nightHawk Response is an incident response forensic framework designed to ingest and analyze Mandiant Redline collection files and FireEye HX audits. It provides a centralized platform for asynchronous forensic data presentation on an Elasticsearch backend, enabling security teams to manage multiple investigations and endpoints from a single interface. The framework solves the problem of fragmented forensic analysis by offering tools for search, timelining, stacking, and tagging across collected data.
Incident responders, digital forensics analysts, and security operations teams who need to process and analyze forensic data from endpoint collections at scale. It is particularly useful for organizations managing numerous investigations simultaneously.
Developers choose nightHawk Response for its ability to handle large-scale forensic data ingestion and presentation with Elasticsearch, offering features like global search, timelining, and tagging in a single pane of glass. Its open-source nature and self-hosted deployment provide control and customization unavailable in proprietary solutions.
Incident Response Forensic Framework
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Leverages Elasticsearch's parent-child document model to handle millions of documents efficiently, as designed for large-scale forensic data ingestion and presentation.
Consolidates multiple audit types into a single view per endpoint, addressing the challenge of managing hundreds of investigations from one pane of glass.
Includes timelining, interactive process trees, and global search for comprehensive analysis, as highlighted in the Key Features section.
Supports both Mandiant Redline and FireEye HX audit files with concurrent or sequential uploads, though with resource limitations noted in the README.
Installation requires managing multiple services like Elasticsearch, Kibana, and RabbitMQ with a script that can fail, as admitted in the setup instructions.
Relies on older components like Django 1.8, with a pending rewrite in React for Version 2.0, indicating maintainability and modern feature gaps.
Concurrent uploads are limited to 5 and can strain hardware, necessitating careful planning for production use, as warned in the Uploading section.