Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F22%3A00125776" target="_blank" >RIV/00216224:14610/22:00125776 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10639-022-11093-6" target="_blank" >https://link.springer.com/article/10.1007/s10639-022-11093-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10639-022-11093-6" target="_blank" >10.1007/s10639-022-11093-6</a>

Result language

angličtina

Original language name

Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training

Original language description

Cybersecurity professionals need hands-on training to prepare for managing the current advanced cyber threats. To practice cybersecurity skills, training participants use numerous software tools in computer-supported interactive learning environments to perform offensive or defensive actions. The interaction involves typing commands, communicating over the network, and engaging with the training environment. The training artifacts (data resulting from this interaction) can be highly beneficial in educational research. For example, in cybersecurity education, they provide insights into the trainees' learning processes and support effective learning interventions. However, this research area is not yet well-understood. Therefore, this paper surveys publications that enhance cybersecurity education by leveraging trainee-generated data from interactive learning environments. We identified and examined 3021 papers, ultimately selecting 35 articles for a detailed review. First, we investigated which data are employed in which areas of cybersecurity training, how, and why. Second, we examined the applications and impact of research in this area, and third, we explored the community of researchers. Our contribution is a systematic literature review of relevant papers and their categorization according to the collected data, analysis methods, and application contexts. These results provide researchers, developers, and educators with an original perspective on this emerging topic. To motivate further research, we identify trends and gaps, propose ideas for future work, and present practical recommendations. Overall, this paper provides in-depth insight into the recently growing research on collecting and analyzing data from hands-on training in security contexts.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Education and Information Technologies

ISSN

1360-2357

e-ISSN

1573-7608

Volume of the periodical

27

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

34

Pages from-to

12179-12212

UT code for WoS article

000805528800005

EID of the result in the Scopus database

2-s2.0-85130845189