scholarly journals Social engineering in cybersecurity: a domain ontology and knowledge graph application examples

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zuoguang Wang ◽  
Hongsong Zhu ◽  
Peipei Liu ◽  
Limin Sun

AbstractSocial 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.

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.


2019 ◽  
Vol 18 (03) ◽  
pp. 953-979 ◽  
Author(s):  
Lingling Zhang ◽  
Minghui Zhao ◽  
Zili Feng

In the era of big data, how to obtain useful knowledge from online news and utilize it as an important basis to make investment decision has become the hotspot of industrial and academic research. At present, there have been research and practice on explicit knowledge acquisition from news, but tacit knowledge acquisition is still under exploration. Based on the general mechanism of domain knowledge, knowledge reasoning, and knowledge discovery, this paper constructs a framework for discovering tacit knowledge from news and applying the knowledge to stock forecasting. The concrete work is as follows: First, according to the characteristics of financial field and the conceptual cube, the conceptual structure of industry–company–product is constructed, and the framework of domain ontology is put forward. Second, with the construction of financial field ontology, the financial news knowledge management framework is proposed. Besides, with the application of attributes in ontology and domain rules extracted from news text, the knowledge reasoning mechanism of financial news is constructed to achieve financial news knowledge discovery. Finally, news knowledge that reflects important information about stock changes is integrated into the traditional stock price forecasting model and the newly proposed model performs well in the empirical analysis of polyester industry.


2021 ◽  
Author(s):  
Jian Xie ◽  
Xi Li ◽  
Da Hong Xu ◽  
Hua Ling Zhou ◽  
Mengzi Liang ◽  
...  

Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.


Author(s):  
Jin-Tan Yang ◽  
Pao Ta Yu ◽  
Nian Shing Chen ◽  
Chun Yen Tsai ◽  
Chi-Chin Lee ◽  
...  

The purpose of this study is to conduct teachers to author a teaching material by using visualized domain ontology as scaffolding. Based on a content repository management system (CRMS), mathematics ontology to support teachers for authoring teaching materials is developed. Although the domain ontology of mathematics at secondary school level in Taiwan provides structured vocabularies for describing domain content, those teachers who want to create a knowledge-rich description of domain knowledge, such as required by the “Semantic Web,” use ontology that turns out to provide only part of knowledge required. In this chapter, we examine problems related to capturing the learning resources or learning objects (LOs) on a CRMS. To construct ontology for a subset of mathematics course descriptions, the representation requirements by resource description framework/resource description framework schema (RDF/RDFS) was implemented. Furthermore, a visualized online authoring tool (VOAT) is designed for authoring teaching materials on the Web. Finally, discussion and future research are addressed.


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