Podrobný výpis o publikaci

ŠVÁBENSKÝ, Valdemar, Jan VYKOPAL a Pavel ČELEDA. Towards Learning Analytics in Cybersecurity Capture the Flag Games. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE’19). 2019. ISBN 978-1-4503-5890-3.
Další formáty: BibTeX LaTeX RIS @proceedings{40057, author = {Švábenský, Valdemar and Vykopal, Jan and Čeleda, Pavel}, booktitle = {Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE’19)}, keywords = {Cybersecurity games; Capture the flag; Learning analytics}, language = {eng}, isbn = {978-1-4503-5890-3}, title = {Towards Learning Analytics in Cybersecurity Capture the Flag Games}, year = {2019} } TY - CONF ID - 40057 AU - Švábenský, Valdemar - Vykopal, Jan - Čeleda, Pavel PY - 2019 TI - Towards Learning Analytics in Cybersecurity Capture the Flag Games SN - 9781450358903 KW - Cybersecurity games KW - Capture the flag KW - Learning analytics N2 - Capture the Flag games are software applications designed to exercise cybersecurity concepts, practice using security tools, and understand cyber attacks and defense. We develop and employ these games at our university for training purposes, unlike in the traditional competitive setting. During the gameplay, it is possible to collect data about players’ in-game actions, such as typed commands or solution attempts, including the timing of these actions. Although such data was previously employed in computer security research, to the best of our knowledge, there were few attempts to use this data primarily to improve education. In particular, we see an open and challenging research problem in creating an artificial intelligence assistant that would facilitate the learning of each player. Our goal is to propose, apply, and experimentally evaluate data analysis and machine learning techniques to derive information about the players' interactions from the in-game data. We want to use this information to automatically provide each player with a personalized formative assessment. Such assessment will help the players identify their mastered concepts and areas for improvement, along with suggestions and actionable steps to take. Furthermore, we want to identify high- or low-performing players during the game, and subsequently, offer them game tasks more suitable to their skill level. These interventions would supplement or even replace feedback from instructors, which would significantly increase the learning impact of the games, enable more students to learn cybersecurity skills at an individual pace, and lower the costs. ER - ŠVÁBENSKÝ, Valdemar, Jan VYKOPAL a Pavel ČELEDA. Towards Learning Analytics in Cybersecurity Capture the Flag Games. In \textit{Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE’19)}. 2019. ISBN~978-1-4503-5890-3.

ŠVÁBENSKÝ, Valdemar, Jan VYKOPAL a Pavel ČELEDA. Towards Learning Analytics in Cybersecurity Capture the Flag Games. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE’19). 2019. ISBN 978-1-4503-5890-3.

Další formáty: BibTeX LaTeX RIS

Základní údaje
Originální název	Towards Learning Analytics in Cybersecurity Capture the Flag Games
Autoři	ŠVÁBENSKÝ, Valdemar (703 Slovensko, garant, domácí), Jan VYKOPAL (203 Česká republika, domácí) a Pavel ČELEDA (203 Česká republika, domácí).
Vydání	Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE’19), 2019.

Další údaje
Originální jazyk	angličtina
Typ výsledku	Konferenční abstrakta
Stát vydavatele	Spojené státy
Utajení	není předmětem státního či obchodního tajemství
Kód RIV	RIV/00216224:14330/19:00108979
Organizace	Fakulta informatiky - Masarykova univerzita
ISBN	978-1-4503-5890-3
Klíčová slova anglicky	Cybersecurity games; Capture the flag; Learning analytics
Návaznosti	MUNI/A/1145/2018.
Změnil	Změnil: RNDr. Daniel Jakubík, učo 139797. Změněno: 7. 9. 2020 06:00.

Anotace
Capture the Flag games are software applications designed to exercise cybersecurity concepts, practice using security tools, and understand cyber attacks and defense. We develop and employ these games at our university for training purposes, unlike in the traditional competitive setting. During the gameplay, it is possible to collect data about players’ in-game actions, such as typed commands or solution attempts, including the timing of these actions. Although such data was previously employed in computer security research, to the best of our knowledge, there were few attempts to use this data primarily to improve education. In particular, we see an open and challenging research problem in creating an artificial intelligence assistant that would facilitate the learning of each player. Our goal is to propose, apply, and experimentally evaluate data analysis and machine learning techniques to derive information about the players' interactions from the in-game data. We want to use this information to automatically provide each player with a personalized formative assessment. Such assessment will help the players identify their mastered concepts and areas for improvement, along with suggestions and actionable steps to take. Furthermore, we want to identify high- or low-performing players during the game, and subsequently, offer them game tasks more suitable to their skill level. These interventions would supplement or even replace feedback from instructors, which would significantly increase the learning impact of the games, enable more students to learn cybersecurity skills at an individual pace, and lower the costs.

Anotace

Capture the Flag games are software applications designed to exercise cybersecurity concepts, practice using security tools, and understand cyber attacks and defense. We develop and employ these games at our university for training purposes, unlike in the traditional competitive setting. During the gameplay, it is possible to collect data about players’ in-game actions, such as typed commands or solution attempts, including the timing of these actions. Although such data was previously employed in computer security research, to the best of our knowledge, there were few attempts to use this data primarily to improve education. In particular, we see an open and challenging research problem in creating an artificial intelligence assistant that would facilitate the learning of each player. Our goal is to propose, apply, and experimentally evaluate data analysis and machine learning techniques to derive information about the players' interactions from the in-game data. We want to use this information to automatically provide each player with a personalized formative assessment. Such assessment will help the players identify their mastered concepts and areas for improvement, along with suggestions and actionable steps to take. Furthermore, we want to identify high- or low-performing players during the game, and subsequently, offer them game tasks more suitable to their skill level. These interventions would supplement or even replace feedback from instructors, which would significantly increase the learning impact of the games, enable more students to learn cybersecurity skills at an individual pace, and lower the costs.