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Log-based cyber threat hunting has emerged as an important solution tocounter sophisticated attacks. However, existing approaches require non-trivialefforts of manual query construction and have overlooked the rich externalthreat knowledge provided by open-source Cyber Threat Intelligence (OSCTI). Tobridge the gap, we propose ThreatRaptor, a system that facilitates threathunting in computer systems using OSCTI. Built upon system auditing frameworks,ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLPpipeline that extracts structured threat behaviors from unstructured OSCTItext, (2) a concise and expressive domain-specific query language, TBQL, tohunt for malicious system activities, (3) a query synthesis mechanism thatautomatically synthesizes a TBQL query for hunting, and (4) an efficient queryexecution engine to search the big audit logging data. Evaluations on a broadset of attack cases demonstrate the accuracy and efficiency of ThreatRaptor inpractical threat hunting.

Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence