A System for Automated Open-Source Threat Intelligence Gathering and Management

• URL: http://arxiv.org/abs/2101.07769v2
• Date: Fri, 26 Feb 2021 20:50:51 GMT
• Title: A System for Automated Open-Source Threat Intelligence Gathering and Management
• Authors: Peng Gao, Xiaoyuan Liu, Edward Choi, Bhavna Soman, Chinmaya Mishra, Kate Farris, Dawn Song
• Abstract summary: SecurityKG is a system for automated OSCTI gathering and management. It uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors.
• Score: 53.65687495231605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To remain aware of the fast-evolving cyber threat landscape, open-source Cyber Threat Intelligence (OSCTI) has received growing attention from the community. Commonly, knowledge about threats is presented in a vast number of OSCTI reports. Despite the pressing need for high-quality OSCTI, existing OSCTI gathering and management platforms, however, have primarily focused on isolated, low-level Indicators of Compromise. On the other hand, higher-level concepts (e.g., adversary tactics, techniques, and procedures) and their relationships have been overlooked, which contain essential knowledge about threat behaviors that is critical to uncovering the complete threat scenario. To bridge the gap, we propose SecurityKG, a system for automated OSCTI gathering and management. SecurityKG collects OSCTI reports from various sources, uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors, and constructs a security knowledge graph. SecurityKG also provides a UI that supports various types of interactivity to facilitate knowledge graph exploration.

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