Threat Report ATT&CK Mapper (TRAM) is an open-source platform designed to reduce cost and increase the effectiveness of integrating ATT&CK across the CTI community. It does this by automating the mapping of cyber threat intelligence (CTI) reports to MITRE ATT&CK®. Threat intel providers, threat intel platforms, and analysts can use TRAM to integrate ATT&CK more easily and consistently into their products.
The platform works out of the box to identify up to 50 common ATT&CK techniques in text documents; it also supports tailoring the model by annotating additional items and rebuilding the model. This Wiki describes the results of the Center for Threat-Informed Defense (CTID) research into automated ATT&CK mapping and provides details and instructions for tailoring the platform to your organization's unique dataset.
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The TRAM web application can be deployed in a containerized environment with Docker or Kubernetes. You should read the installation instructions to make sure that you are comfortable with the prerequisites. Alternatively, if you want to focus on Machine Learning Engineering, you can run the project notebooks for fine tuning your own models.
| Resource | Description |
|---|---|
| Documentation | Complete documentation for TRAM |
| Installation | Instructions for downloading and installing TRAM container images |
| Notebooks | Jupyter notebooks for SciBERT-based single-label and multi-label models. |
| Developer Setup | Instructions for contributing code changes to TRAM. Only required for local development and testing. |
There are several ways that you can get involved with this project and help advance threat-informed defense:
- Install the TRAM web application and try processing CTI reports. We welcome your feedback on the effectiveness of using machine learning to identify TTPs in human-readable text.
- Share your use cases. We are interested in developing additional tools and resources to help the community understand and make threat-informed decisions in their risk management programs. If you have ideas or suggestions, we consider them as we explore additional research projects.
- Label your own data and use the notebooks to fine tune your own models. This is a complex undertaking, but it allows you to adapt TRAM to your own environment and data. If you have high end GPUs in your environment, you can run these notebooks on your own instrastructure; otherwise you can run them on the paid or free tiers of Google Colab.
Please submit issues for any technical questions/concerns or contact [email protected] directly for more general inquiries.
Also see the guidance for contributors if are you interested in contributing or simply reporting issues.
We welcome your feedback and contributions to help advance TRAM. Please see the guidance for contributors if are you interested in contributing or simply reporting issues.
To contribute training data, see the Data Annotation wiki.
Please submit issues for any technical questions/concerns or contact [email protected] directly for more general inquiries.
©2023 MITRE Engenuity, LLC. Approved for Public Release. Document numbers CT0035, CT0075.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
This project makes use of MITRE ATT&CK®