A new entity prediction method for cyber threat intelligence

Qing Wang; Xu Ji; Dongxu Sun; Jianyi Zhang

doi:10.1117/12.2681563

1 June 2023 A new entity prediction method for cyber threat intelligence

Qing Wang, Xu Ji, Dongxu Sun, Jianyi Zhang

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 1271821 (2023) https://doi.org/10.1117/12.2681563
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

With the diversification and rapid development of the network, the generation of cyber threat intelligence (CTI) is more and more comprehensive, and the knowledge graph of which is also developed and updated constantly. Of course, it is inevitable to encounter the expansion of the knowledge base of threat intelligence, and Intelligence entities are closely connected. In the Knowledge Graph (KG) field, link prediction is widespread because the entities are already available. And you need to determine the relationship between the two entities somehow. However, the more complex problem is extending the entities created after establishing the knowledge base into the existing one. Dealing with these problems often relies on extracting entities and relationships from the natural language of threat intelligence, but this approach only predicts undeclared intelligence entities or relationships. Embedded models often use structural rules to predict link relationships but cannot predict missing intelligence entities. In this paper, the latest MiNer method is combined with the effective STransE model to fully invoke the neighbor information around the entity and analyze the proportion of each neighbor. At the same time, this paper uses neighbor traffic statistics to assign corresponding weights to indicate which neighbor is more important to the entity to be predicted. We show that our method can use the existing cyber threat intelligence KG database to effectively predict some unknown entities, according to the importance of neighbor information, and add them to the existing KG database. At the same time, our method’s performance is greatly improved when the specific matrix is converted to the diagonal matrix.

Citation Download Citation

Qing Wang, Xu Ji, Dongxu Sun, and Jianyi Zhang "A new entity prediction method for cyber threat intelligence", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 1271821 (1 June 2023); https://doi.org/10.1117/12.2681563

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KEYWORDS

Matrices

Information security

Mining

Databases

Neural networks

Performance modeling

Defense and security

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