NeoCyberKG: Enhancing Cybersecurity Laboratories with a Machine Learning-enabled Knowledge Graph

Yuli Deng, Zhen Zeng, Dijiang Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The hands-on lab is a critical component of cybersecurity education. There lacks of a coherent way to manage existing labs to provide a practical learning plan for learners in the cybersecurity area. Previous studies utilized the word embedding technologies to construct a knowledge graph and adopt it as a learning guide for students, but this approach has its limitations. In this paper, we present a new approach based on latent semantic analysis (LSA) method to replace word embedding in previous studies as it is more appropriate in a small-size corpus, and it is also able to create a mapping that connects both the topic of each lab and concepts contained in each lab. We use LSA to identify relevant semantic relations, extract relevant lab problems, and construct knowledge graphs from lab contents related to cybersecurity topics. We utilize the output of this study by establishing a web-based lab environment for students that: 1. providing lab index and searching, which contains concepts and knowledge extract from each lab. 2.building a recommendation/guidance system for cybersecurity labs and suggesting more relevant labs based on users learning preferences and past lab history to maximize learning outcomes. To measure the effectiveness of the proposed solution, we conducted a use case study and collected survey data from a graduate-level cybersecurity class at a public university. Our study shows that users tend to gain enhanced learning outcomes and express more interest in the cybersecurity area by leveraging the knowledge graph as a learning guide.

Original languageEnglish (US)
Title of host publicationITiCSE 2021 - Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education
PublisherAssociation for Computing Machinery
Pages310-316
Number of pages7
ISBN (Electronic)9781450382144
DOIs
StatePublished - Jun 26 2021
Event26th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2021 - Virtual, Online, Germany
Duration: Jun 26 2021Jul 1 2021

Publication series

NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
ISSN (Print)1942-647X

Conference

Conference26th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2021
Country/TerritoryGermany
CityVirtual, Online
Period6/26/217/1/21

Keywords

  • cybersecurity
  • knowledge graph
  • laboratory

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Education

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