@inproceedings{lange-etal-2024-annoctr,
    title = "{A}nno{CTR}: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports",
    author = {Lange, Lukas  and
      M{\"u}ller, Marc  and
      Haratinezhad Torbati, Ghazaleh  and
      Milchevski, Dragan  and
      Grau, Patrick  and
      Pujari, Subhash Chandra  and
      Friedrich, Annemarie},
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.103",
    pages = "1147--1160",
    abstract = "Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT{\&}CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT{\&}CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT{\&}CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT{\&}CK are an effective source for training data augmentation.",
}

Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT{\&}CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT{\&}CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT{\&}CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT{\&}CK are an effective source for training data augmentation.
Lange, Lukas M{\"u}ller, Marc Haratinezhad Torbati, Ghazaleh Milchevski, Dragan Grau, Patrick Pujari, Subhash Ch ra Friedrich, Annemarie
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports