Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
ArXiv:
License:
| license: apache-2.0 | |
| tags: | |
| - threat intelligence | |
| - cyber security | |
| - STIX standard | |
| - MITRE ATT&CK | |
| task_categories: | |
| - text-generation | |
| language: | |
| - en | |
| size_categories: | |
| - 10K<n<100K | |
| # AZERG-Dataset | |
| This repository contains the AZERG-Dataset, a comprehensive collection of annotated cyber threat intelligence (CTI) reports designed for training and evaluating models on STIX entity and relationship extraction. | |
| This dataset was created for the paper: "From Text to Actionable Intelligence: Automating STIX Entity and Relationship Extraction". It is the largest publicly available dataset of its kind, meticulously annotated with STIX-compliant entities and relationships to facilitate the development of automated threat intelligence tools. | |
| # π Dataset Overview | |
| The AZERG-Dataset is built from 141 real-world threat analysis reports and contains 4,011 STIX entities and 2,075 STIX relationships. It was curated to address the lack of training data for automated STIX report generation and supports a multi-task approach to threat intelligence extraction. | |
| The extraction process is divided into four sequential subtasks: | |
| - T1: Entity Detection: Identifying all STIX entities (SDOs and SCOs) in a text passage. | |
| - T2: Entity Type Identification: Assigning a specific STIX type to each detected entity. | |
| - T3: Related Pair Detection: Identifying which pairs of entities are semantically related based on the text. | |
| - T4: Relationship Type Identification: Determining the precise STIX relationship type (e.g., uses, targets) between a related pair of entities. | |
| ## π Dataset Structure | |
| The dataset is organized into train and test splits. The training and testing data are sourced from completely non-overlapping reports and vendors to ensure a robust evaluation of model generalization. | |
| ``` | |
| AZERG-Dataset/ | |
| βββ train/ | |
| β βββ azerg_T1_train.json | |
| β βββ azerg_T2_train.json | |
| β βββ azerg_T3_train.json | |
| β βββ azerg_T4_train.json | |
| β βββ azerg_MixTask_train.json # Combined data for all tasks | |
| βββ test/ | |
| βββ annoctr_T1_test.json | |
| βββ annoctr_T2_test.json | |
| βββ annoctr_T3_test.json | |
| βββ annoctr_T4_test.json | |
| βββ azerg_T1_test.json | |
| βββ azerg_T2_test.json | |
| βββ azerg_T3_test.json | |
| βββ azerg_T4_test.json | |
| ``` | |
| ## π Citation | |
| If you use this dataset in your research, please cite the original paper (ArXiv for now, the paper is accepted at RAID 2025): | |
| ``` | |
| @article{lekssays2025azerg, | |
| title={From Text to Actionable Intelligence: Automating STIX Entity and Relationship Extraction}, | |
| author={Lekssays, Ahmed and Sencar, Husrev Taha and Yu, Ting}, | |
| journal={arXiv preprint arXiv:2507.16576}, | |
| year={2025} | |
| } | |
| ``` |