| | --- |
| | license: cc-by-4.0 |
| | task_categories: |
| | - token-classification |
| | language: |
| | - en |
| | tags: |
| | - cyber |
| | pretty_name: Anno CTR |
| | --- |
| | |
| | <!--- |
| |
|
| | Copyright (c) 2023 Robert Bosch GmbH and its subsidiaries. |
| | |
| | --> |
| |
|
| | # AnnoCTR Resources |
| |
|
| | This repository contains the companion material for the following publication: |
| |
|
| | > Lukas Lange, Marc Müller, Ghazaleh Haratinezhad Torbati, Dragan Milchevski, Patrick Grau, Subhash Pujari, Annemarie Friedrich. **AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports.** LREC-COLING 2024. |
| |
|
| | Please cite this paper if using the dataset or the code, and direct any questions to [Lukas Lange](mailto:lukas.lange@de.bosch.com). |
| | The paper can be found at the [ACL Anthology (tbd.)](https://www.aclweb.org/anthology/TODO/) or at |
| | [ArXiv](https://arxiv.org/abs/2404.07765). |
| |
|
| |
|
| | ## Purpose of this Software |
| |
|
| | This software is a research prototype, solely developed for and published as |
| | part of the publication cited above. It will neither be maintained nor monitored in any way. |
| |
|
| |
|
| | ## The AnnoCTR Corpus |
| |
|
| | AnnoCTR consists of 400 cyber threat reports that have been obtained from commercial CTI vendors. |
| | The reports describe threat-related information such as tactics, techniques, actors, tools, and targeted industries. |
| | The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts. |
| | The annotations include mentions of organizations, locations, industry sectors, time expressions, |
| | code snippets, hacker groups, malware, tools, tactics, and techniques. |
| |
|
| | The dataset is split into three parts: train, dev, and test, with 60%, 15%, and 25% of the documents, |
| | respectively. |
| | The train set is used for model training, the dev set is used for model selection, |
| | and the test set is used for evaluation. |
| |
|
| | For further information on the annotation scheme, |
| | please refer to our paper and the annotation guidelines for the |
| | [general concepts](Annotation_Guidelines_General_Layer.pdf) and |
| | [cybersecurity-specific concepts](Annotation_Guidelines_Cysec_Layer.pdf). |
| |
|
| | ### Corpus File Formats |
| |
|
| | This is an HF friendly dataset loader for training LLMs on the NER tags. |
| |
|
| | ### Usage Example |
| |
|
| | ``` |
| | from datasets import load_dataset |
| | |
| | annodb = load_dataset("priamai/AnnoCTR","all_tags",trust_remote_code=True) |
| | |
| | label_list = annodb["train"].features[f"all_tags"].feature.names |
| | |
| | |
| | ``` |
| | The letter that prefixes each `tag` indicates the token position of the entity: |
| |
|
| | - `B-` indicates the beginning of an entity. |
| | - `I-` indicates a token is contained inside the same entity (for example, the `State` token is a part of an entity like |
| | `Empire State Building`). |
| | - `0` indicates the token doesn't correspond to any entity. |
| |
|
| | There are 23 tags currently. |
| |
|
| | ### Acknowledgements |
| |
|
| | The reports have been kindly donated by |
| | [Intel471](https://intel471.com/blog}), |
| | [Lab52](https://lab52.io/blog/), |
| | the [threat intelligence division of S2 Grupo](https://s2grupo.es/), |
| | [Proofpoint](https://www.proofpoint.com/us/blog), |
| | [QuoIntelligence](https://quointelligence.eu/blog), |
| | and [ZScaler](https://www.zscaler.com/blogs/security-research). |
| |
|
| |
|
| | ## License |
| |
|
| | The AnnoCTR corpus located in the folder [AnnoCTR](AnnoCTR) is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/) (CC-BY-SA 4.0). |
| |
|
