Relation Extraction Techniques in Cyber Threat Intelligence

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AJQI322CG" target="_blank" >RIV/00216208:11320/25:JQI322CG - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205348424&doi=10.1007%2f978-3-031-70239-6_24&partnerID=40&md5=18066e24e82a217d1272c0a3ba676677" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205348424&doi=10.1007%2f978-3-031-70239-6_24&partnerID=40&md5=18066e24e82a217d1272c0a3ba676677</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-70239-6_24" target="_blank" >10.1007/978-3-031-70239-6_24</a>

Result language
angličtina
Original language name
Relation Extraction Techniques in Cyber Threat Intelligence
Original language description
Cyber Threat Intelligence (CTI) provides a structured and interconnected model for threat information through Cybersecurity Knowledge Graphs. This allows researchers and practitioners to represent and organize complex relationships and entities in a more coherent form. Above all, the discovery of hidden relationships between different CTI entities, such as threat actors, malware, infrastructure, and attacks, is becoming a crucial task in this domain, facilitating proactive defense measures and helping to identify Tactics, Techniques, and Procedures (TTPs) employed by malicious parties. In this paper, we provide a Systematization of Knowledge (SoK) to analyze the existing literature and give insights into the important CTI task of Relation Extraction. In particular, we design a categorization of the relations used in CTI; we analyze the techniques employed for their extraction, the emerging trends and open issues in this context, and the main future directions. This work provides a novel and fresh perspective that can help the reader understand how relationships among entities can be schematized to provide a better view of the cyber threat landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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