An Automatic Generation Approach of the Cyber Threat Intelligence Records Based on Multi-Source Information Fusion

Tianfang Sun, Pin Yang, Mengming Li and Shan Liao
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Tianfang Sun: College of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
Pin Yang: College of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
Mengming Li: College of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
Shan Liao: College of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China

Future Internet, 2021, vol. 13, issue 2, 1-19

Abstract: With the progressive deterioration of cyber threats, collecting cyber threat intelligence (CTI) from open-source threat intelligence publishing platforms (OSTIPs) can help information security personnel grasp public opinions with specific pertinence, handle emergency events, and even confront the advanced persistent threats. However, due to the explosive growth of information shared on multi-type OSTIPs, manually collecting the CTI has had low efficiency. Articles published on the OSTIPs are unstructured, leading to an imperative challenge to automatically gather CTI records only through natural language processing (NLP) methods. To remedy these limitations, this paper proposes an automatic approach to generate the CTI records based on multi-type OSTIPs (GCO), combing the NLP method, machine learning method, and cybersecurity threat intelligence knowledge. The experiment results demonstrate that the proposed GCO outperformed some state-of-the-art approaches on article classification and cybersecurity intelligence details (CSIs) extraction, with accuracy, precision, and recall all over 93%; finally, the generated records in the Neo4j-based CTI database can help reveal malicious threat groups.

Keywords: cyber threat intelligence; open-source threat intelligence platform; nature language processing; machine learning; information extraction; text analytics (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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