madox81/Adema_ATTACK_DS

Tasks

• text-classification
• text-generation

Citation text from the card

```bibtex
@article{LI2024103815,
title = {Automated discovery and mapping ATT&CK tactics and techniques for unstructured cyber threat intelligence},
journal = {Computers & Security},
volume = {140},
pages = {103815},
year = {2024},
issn = {0167-4048},
doi = {https://doi.org/10.1016/j.cose.2024.103815},
url = {https://www.sciencedirect.com/science/article/pii/S0167404824001160},
author = {Lingzi Li and Cheng Huang and Junren Chen},
keywords = {Cyber threat intelligence, MITRE ATT&CK, Multi-label classification, Network security},
abstract = {As cyber attacks are growing, Cyber Threat Intelligence (CTI) enhances the ability of security systems to resist novel cyber threats. However, since most CTI is unstructured data written in natural language, it needs to be understood and summarized by security experts to be effectively utilized. To address the problem, we adopt the ATT&CK matrix as the taxonomy to propose a method for automated mapping of unstructured threat intelligence to tactics and techniques. The proposed method contains a pre-processor for text denoising, a label extractor for classifying which tactics and techniques category the text belongs to, and a post-processor for correcting the classification results. The label extractor consists of two multi-label classifiers based on DistilBERT for tactics and techniques classification respectively. The post-processor corrects the classification results based on the relations between tactics, techniques, and sub-techniques 

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