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AI in Cybersecurity Education- A Systematic Literature Review of Studies on Cybersecurity MOOCs

Samuli Laato, Ali Farooq, Henri Tenhunen, Tinja Pitkämäki, Antti Hakkala, Antti Airola

202034 citationsDOI

Abstract

Machine learning (ML) techniques are changing both the offensive and defensive aspects of cybersecurity. The implications are especially strong for privacy, as ML approaches provide unprecedented opportunities to make use of collected data. Thus, education on cybersecurity and AI is needed. To investigate how AI and cybersecurity should be taught together, we look at previous studies on cybersecurity MOOCs by conducting a systematic literature review. The initial search resulted in 72 items and after screening for only peer-reviewed publications on cybersecurity online courses, 15 studies remained. Three of the studies concerned multiple cybersecurity MOOCs whereas 12 focused on individual courses. The number of published work evaluating specific cybersecurity MOOCs was found to be small compared to all available cybersecurity MOOCs. Analysis of the studies revealed that cybersecurity education is, in almost all cases, organised based on the topic instead of used tools, making it difficult for learners to find focused information on AI applications in cybersecurity. Furthermore, there is a gab in academic literature on how AI applications in cybersecurity should be taught in online courses.

Topics & Concepts

OffensiveComputer securityComputer scienceWork (physics)EngineeringOperations researchMechanical engineeringAdvancements in Semiconductor Devices and Circuit DesignAdvanced Malware Detection TechniquesNetwork Security and Intrusion Detection
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