The Technique Inference Engine (TIE) allows cyber defenders to forecast an adversary's next steps by predicting associated MITRE ATT&CK techniques from previously observed techniques. TIE enables defenders to build a complete picture of an adversary and their actions. TIE also offers one of the largest publicly available datasets of its kind, linking CTI Reports to ATT&CK Techniques. The dataset includes 43,899 technique observations across 6,236 CTI Reports, achieving 96% coverage of ATT&CK Enterprise v15.0. This project is created and maintained by the MITRE Engenuity Center for Threat-Informed Defense in furtherance of our mission to advance the start of the art and the state of the practice in threat-informed defense globally. The project is funded by our research participants.
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To get started, we suggest visiting the project website, reading about the project, and experimenting with the Engine. For machine learning engineers, you may want to try training your own models using the Python notebook and/or training data.
| Resource | Description |
|---|---|
| Technique Inference Engine (Website) | Learn about the project and run TIE right in your browser. |
| Technique Inference Engine (Python Notebook) | Run TIE locally and modify the code or training data to build custom models. |
| Training Data | Access the CTI Data used to train TIE. |
There are several ways that you can get involved with this project and help advance threat-informed defense:
- Visit the Technique Inference Engine website. Use the website to learn how the Engine works and make your own predictions.
- Train your own Engine. Train the Technique Inference Engine on your own CTI data using the official Python Notebook.
- Contribute your own CTI. We are interested in further expanding the Engine's dataset. If you have your own CTI you'd like to share, we would welcome your contribution.
We welcome your feedback and contributions to help advance Technique Inference Engine. Please see the guidance for contributors if are you interested in contributing or simply reporting issues.
Please submit issues for any technical questions/concerns or contact ctid@mitre-engenuity.org directly for more general inquiries.
Copyright 2024 MITRE Engenuity. Approved for public release. Document number CT0124.
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
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