Student assessment in cybersecurity training automated by pattern mining and clustering

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...

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Veröffentlicht in:Education and information technologies Jg. 27; H. 7; S. 9231 - 9262
Hauptverfasser: Švábenský, Valdemar, Vykopal, Jan, Čeleda, Pavel, Tkáčik, Kristián, Popovič, Daniel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2022
Springer
Springer Nature B.V
Schlagworte:
Analysis
Artificial Intelligence
Automation
Clustering
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computer Security
Computers and Education
Cybersecurity
Cyberterrorism
Data Analysis
Data Collection
Data mining
Data science
Data security
Education
Educational Environment
Educational Researchers
Educational Technology
Information Retrieval
Information Security
Information Systems Applications (incl.Internet)
Interactive learning
Internet
Learning Processes
Machine learning
Pattern Recognition
Safety and security measures
Student Behavior
Student Evaluation
Training
User Interfaces and Human Computer Interaction
New York
Data science
Educational data mining
Learning analytics
Security training
Cybersecurity education
ISSN:1360-2357, 1573-7608
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Zusammenfassung: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.
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ISSN:1360-2357
1573-7608
DOI:10.1007/s10639-022-10954-4