Peipei Liu

Peipei Liu, Hong Li, Zhiyu Wang et al.

Enterprise relation extraction aims to detect pairs of enterprise entities and identify the business relations between them from unstructured or semi-structured text data, and it is crucial for several real-world applications such as risk analysis, rating research and supply chain security. However, previous work mainly focuses on getting attribute information about enterprises like personnel and corporate business, and pays little attention to enterprise relation extraction. To encourage further progress in the research, we introduce the CEntRE, a new dataset constructed from publicly available business news data with careful human annotation and intelligent data processing. Extensive experiments on CEntRE with six excellent models demonstrate the challenges of our proposed dataset.

Zuoguang Wang, Hongsong Zhu, Peipei Liu et al.

Social engineering has posed a serious threat to cyberspace security. To protect against social engineering attacks, a fundamental work is to know what constitutes social engineering. This paper first develops a domain ontology of social engineering in cybersecurity and conducts ontology evaluation by its knowledge graph application. The domain ontology defines 11 concepts of core entities that significantly constitute or affect social engineering domain, together with 22 kinds of relations describing how these entities related to each other. It provides a formal and explicit knowledge schema to understand, analyze, reuse and share domain knowledge of social engineering. Furthermore, this paper builds a knowledge graph based on 15 social engineering attack incidents and scenarios. 7 knowledge graph application examples (in 6 analysis patterns) demonstrate that the ontology together with knowledge graph is useful to 1) understand and analyze social engineering attack scenario and incident, 2) find the top ranked social engineering threat elements (e.g. the most exploited human vulnerabilities and most used attack mediums), 3) find potential social engineering threats to victims, 4) find potential targets for social engineering attackers, 5) find potential attack paths from specific attacker to specific target, and 6) analyze the same origin attacks.