Log-based cyber threat hunting has emerged as an important solution to counter sophisticated attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external threat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we propose ThreatRaptor, a system that facilitates threat hunting in computer systems using OSCTI. Built upon system auditing frameworks, ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, (3) a query synthesis mechanism that automatically synthesizes a TBQL query for hunting, and (4) an efficient query execution engine to search the big audit logging data. Evaluations on a broad set of attack cases demonstrate the accuracy and efficiency of ThreatRaptor in practical threat hunting.
@article{arxiv.2010.13637,
title = {Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence},
author = {Peng Gao and Fei Shao and Xiaoyuan Liu and Xusheng Xiao and Zheng Qin and Fengyuan Xu and Prateek Mittal and Sanjeev R. Kulkarni and Dawn Song},
journal= {arXiv preprint arXiv:2010.13637},
year = {2021}
}