ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866913568153141248 |
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| author | Gao, Peng Liu, Xiaoyuan Choi, Edward Ma, Sibo Yang, Xinyu Song, Dawn |
| author_facet | Gao, Peng Liu, Xiaoyuan Choi, Edward Ma, Sibo Yang, Xinyu Song, Dawn |
| contents | Open-source cyber threat intelligence (OSCTI) has become essential for keeping up with the rapidly changing threat landscape. However, current OSCTI gathering and management solutions mainly focus on structured Indicators of Compromise (IOC) feeds, which are low-level and isolated, providing only a narrow view of potential threats. Meanwhile, the extensive and interconnected knowledge found in the unstructured text of numerous OSCTI reports (e.g., security articles, threat reports) available publicly is still largely underexplored.
To bridge the gap, we propose ThreatKG, an automated system for OSCTI gathering and management. ThreatKG efficiently collects a large number of OSCTI reports from multiple sources, leverages specialized AI-based techniques to extract high-quality knowledge about various threat entities and their relationships, and constructs and continuously updates a threat knowledge graph by integrating new OSCTI data. ThreatKG features a modular and extensible design, allowing for the addition of components to accommodate diverse OSCTI report structures and knowledge types. Our extensive evaluations demonstrate ThreatKG's practical effectiveness in enhancing threat knowledge gathering and management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_10388 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management Gao, Peng Liu, Xiaoyuan Choi, Edward Ma, Sibo Yang, Xinyu Song, Dawn Cryptography and Security Databases Open-source cyber threat intelligence (OSCTI) has become essential for keeping up with the rapidly changing threat landscape. However, current OSCTI gathering and management solutions mainly focus on structured Indicators of Compromise (IOC) feeds, which are low-level and isolated, providing only a narrow view of potential threats. Meanwhile, the extensive and interconnected knowledge found in the unstructured text of numerous OSCTI reports (e.g., security articles, threat reports) available publicly is still largely underexplored. To bridge the gap, we propose ThreatKG, an automated system for OSCTI gathering and management. ThreatKG efficiently collects a large number of OSCTI reports from multiple sources, leverages specialized AI-based techniques to extract high-quality knowledge about various threat entities and their relationships, and constructs and continuously updates a threat knowledge graph by integrating new OSCTI data. ThreatKG features a modular and extensible design, allowing for the addition of components to accommodate diverse OSCTI report structures and knowledge types. Our extensive evaluations demonstrate ThreatKG's practical effectiveness in enhancing threat knowledge gathering and management. |
| title | ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management |
| topic | Cryptography and Security Databases |
| url | https://arxiv.org/abs/2212.10388 |