Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering

Autor/inn/en	Švábenský, Valdemar; Vykopal, Jan; Celeda, Pavel; Tkácik, Kristián; Popovic, Daniel
Titel	Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering
Quelle	In: Education and Information Technologies, 27 (2022) 7, S.9231-9262 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Švábenský, Valdemar)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-10954-4
Schlagwörter	Computer Security; Information Security; Training; Data Collection; Artificial Intelligence; Educational Technology; Data Analysis; Student Behavior; Student Evaluation; Information Retrieval; Pattern Recognition + Suchen Sie Ihr Suchwort? Computervirus; Computersicherheit; Ausbildung; Data capture; Datensammlung; Künstliche Intelligenz; Unterrichtsmedien; Auswertung; Student behaviour; Schülerverhalten; Schulnote; Studentische Bewertung; Mustererkennung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01