Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

Kim, Jongmo; Kim, Kunyoung; Sohn, Mye M.; Park, Gyudong

doi:10.3390/su14148877

Cited by 3 publications

(3 citation statements)

References 58 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each CVE list, the similarity values for the security newsgroups were added together and reflected in the security vulnerability similarity (SVS), as shown in Equation (3).…”

Section: Updates Andmentioning

confidence: 99%

“…In addition, they offer the great advantage of analyzing large amounts of data, such as security logs and network traffic, to detect previously undiscovered threats. In particular, knowledge-based intelligent security systems using ontology or knowledge graph technology are crucial for deepening the understanding of the meaning of and relationships between security data and detecting and responding to complex threats more effectively [3]. Knowledge-based security technologies formally define entities, such as attack types, vulnerabilities, and attackers, and their relationships.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Kim,

Choi

2023

Electronics

View full text Add to dashboard Cite

Recently, security attacks occurring in edge computing environments have emerged as an important research topic in the field of cybersecurity. Edge computing is a distributed computing technology that expands the existing cloud computing architecture to introduce a new layer, the edge layer, between the cloud layer and the user terminal layer. Edge computing has the advantage of greatly improving the data processing speed and efficiency but, at the same time, is complex, and various new attacks occur frequently. Therefore, for improving the security of edge computing, effective and intelligent security strategies and policies must be established in consideration of a wide range of vulnerabilities. Intelligent security systems, which have recently been studied, provide a way to detect and respond to security threats by integrating the latest technologies, such as machine learning and big data analysis. Intelligent security technology can quickly recognize attack patterns or abnormal behaviors within a large amount of data and continuously respond to new threats through learning. In particular, knowledge-based technologies using ontology or knowledge graph technology play an important role in more deeply understanding the meaning and relationships between of security data and more effectively detecting and responding to complex threats. This study proposed a method for recommending strategies to respond to edge computing security incidents based on the automatic generation and embedding of security knowledge graphs. An EdgeSecurity–BERT model, utilizing the latest security vulnerability data from edge computing, was designed to extract entities and their relational information. Also, a security vulnerability assessment method was proposed to recommend strategies to respond to edge computing security incidents through knowledge graph embedding. In the experiment, the classification accuracy of security news data for common vulnerability and exposure data was approximately 86% on average. In addition, the EdgeSecurityKG applying the security vulnerability similarity improved the Hits@10 performance to identify the correct link, but the MR performance was degraded owing to the increased complexity. In complex areas, such as security, careful evaluation of the model’s performance and data selection are important. The EdgeSecurityKG applying the security vulnerability similarity provides an important advantage in understanding complex security vulnerability relationships.

show abstract

“…For each CVE list, the similarity values for the security newsgroups were added together and reflected in the security vulnerability similarity (SVS), as shown in Equation (3).…”

Section: Updates Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Kim,

Choi

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Large-scale knowledge graphs are constructed using information that can be accessed through the web, such as news, blogs, social media, and Wikipedia. These knowledge graphs are used as key resources for services in a variety of application fields, such as recommendation systems, QA, and conversation systems [10,11]. Automatic knowledge graph generation and expansion methods are essential techniques for extracting new knowledge from various and vastly added data and continuously updating existing knowledge graphs.…”

Section: Automatic Knowledge Graph Generation and Expansionmentioning

confidence: 99%

Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Choi¹

2022

Sustainability

View full text Add to dashboard Cite

Knowledge bases built in the knowledge processing field have a problem in that experts have to add rules or update them through modifications. To solve this problem, research has been conducted on knowledge graph expansion methods using deep learning technology, and in recent years, many studies have been conducted on methods of generating knowledge bases by embedding the knowledge graph’s triple information in a continuous vector space. In this paper, using a research literature summary, we propose a domain-specific knowledge graph expansion method based on graph embedding. To this end, we perform pre-processing and process and text summarization with the collected research literature data. Furthermore, we propose a method of generating a knowledge graph by extracting the entity and relation information and a method of expanding the knowledge graph using web data. To this end, we summarize research literature using the Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) model based on domain-specific research literature data and design a Research-BERT (RE-BERT) model that extracts entities and relation information, which are components of the knowledge graph, from the summarized research literature. Moreover, we proposed a method of expanding related entities based on Google news after extracting related entities through the web for the entities in the generated knowledge graph. In the experiment, we measured the performance of summarizing research literature using the BERTSUM model and the accuracy of the knowledge graph relation extraction model. In the experiment of removing unnecessary sentences from the research literature text and summarizing them in key sentences, the result shows that the BERTSUM Classifier model’s ROUGE-1 precision is 57.86%. The knowledge graph extraction performance was measured using the mean reciprocal rank (MRR), mean rank (MR), and HIT@N rank-based evaluation metric. The knowledge graph extraction method using summarized text showed superior performance in terms of speed and knowledge graph quality.

show abstract

Reusable Ontology Generation and Matching from Simulation Models

Tu,

Ehm,

Ismail

et al. 2023

2023 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

Cited by 3 publications

References 58 publications

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

Graph Embedding-Based Domain-Specific Knowledge Graph Expansion Using Research Literature Summary

Reusable Ontology Generation and Matching from Simulation Models

Contact Info

Product

Resources

About