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arXiv · 2101.06761

A System for Efficiently Hunting for Cyber Threats in Computer Systems Using Threat Intelligence

Peng Gao·Fei Shao·Xiaoyuan Liu·Xusheng Xiao·Haoyuan Liu·Zheng Qin·Fengyuan Xu·Prateek Mittal·Sanjeev R. Kulkarni·Dawn Song
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Abstract

Log-based cyber threat hunting has emerged as an important solution to counter sophisticated cyber attacks. However, existing approaches require non-trivial efforts of manual query construction and have overlooked the rich external knowledge about threat behaviors provided by open-source Cyber Threat Intelligence (OSCTI). To bridge the gap, we build ThreatRaptor, a system that facilitates cyber threat hunting in computer systems using OSCTI. Built upon mature 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 from the extracted threat behaviors, and (4) an efficient query execution engine to search the big system audit logging data.

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Peng Gao, Fei Shao, Xiaoyuan Liu, Xusheng Xiao, Haoyuan Liu, Zheng Qin, Fengyuan Xu, Prateek Mittal, Sanjeev R. Kulkarni, Dawn Song. 2021-01-17. A System for Efficiently Hunting for Cyber Threats in Computer Systems Using Threat Intelligence. https://arxiv.org/abs/2101.06761

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