Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F22%3A00125355" target="_blank" >RIV/00216224:14610/22:00125355 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s10639-022-10954-4" target="_blank" >http://dx.doi.org/10.1007/s10639-022-10954-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10639-022-10954-4" target="_blank" >10.1007/s10639-022-10954-4</a>

Result language

angličtina

Original language name

Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering

Original language description

Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.

Czech name

—

Czech description

—

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

7

Country of publishing house

US - UNITED STATES

Number of pages

32

Pages from-to

9231-9262

UT code for WoS article

000775723900004

EID of the result in the Scopus database

2-s2.0-85127380919