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In an age of pervasive connectivity and rapidly evolving cyber threats, cybersecurity has become a transversal competency in engineering education rather than a niche specialism. This paper presents MoonPhase, a multifunctional portable device based on a Raspberry Pi platform, designed to support experiential cybersecurity learning in engineering education contexts by integrating offensive, defensive, and educational modes into a single physical artefact. The work builds on a structured literature review, with study identification and selection documented using a PRISMA 2020-informed flow diagram, and a state-of-the-art analysis of portable cybersecurity tools to derive design requirements that balance realism, safety, and pedagogical alignment. MoonPhase combines sub-GHz replay capabilities, 2.4 GHz interference and monitoring, fake Wi-Fi access points, packet sniffing, and network scanning in a compact, menu-driven platform that can be deployed in regular laboratories and outreach workshops. The paper describes the hardware and software architecture of the device and its instructional framing, linking device functionalities to learning outcomes, laboratory activities, ethical reflection, and assessment. A preliminary educational pilot was conducted with 21 undergraduate students using a 90 min session and a one-group pre-test/post-test design. Mean knowledge scores increased from 5.86±1.42 to 8.24±1.00, corresponding to an average gain of 2.38 points. A paired-samples t-test confirmed a statistically significant difference (t(20)=14.744, p<0.001), with a large within-participant effect size (Cohen’s dz=3.217). Students also reported high levels of perceived authenticity (4.38/5), engagement (4.57/5), and self-reported ethical awareness (4.29/5). These exploratory results provide preliminary empirical support for the proposed instructional framework, while the small sample size and absence of a control group limit causal interpretation. MoonPhase is positioned as a replicable educational resource that brings students closer to realistic attack and defence scenarios in controlled settings.
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Silva et al. (2026) studied this question.
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