Cybersecurity Awareness Through Interactive Learning Using the CyberVigilance Game

Abstract

Cybersecurity has become increasingly important in today’s digital landscape, with end users bearing a major duty to ensure the security of computer systems. A significant percent of data breaches are associated with human involvement, highlighting the crucial role individuals play in cybersecurity and the necessity of developing practical solutions to mitigate security risks associated with human factors. Traditional training approaches often fail to adequately address cybersecurity-related human errors due to low engagement levels and lack of interactivity. To address these shortcomings, this research introduces 'CyberVigilance,' an instructional cybersecurity game designed for students. It is implemented as an interactive educational game to teach cybersecurity principles. The game contributes to cybersecurity awareness by offering students an engaging, hands-on learning experience. The feedback and scoring mechanisms within the game reinforce the importance of cybersecurity awareness, motivating students to apply what they have learned in practical contexts. Using a multi-agent system (MAS), CyberVigilance integrates cards and feedback to represent various cybersecurity scenarios in a competitive game where students act as defenders against computer-simulated attacks. Students earn points by selecting cards linked to cybersecurity awareness, which enhances their decision-making skills and prepares them for real-world cybersecurity threats. Most importantly, the game captures data on students' performance, which is then analyzed to assess the effectiveness of the MAS in predicting and classifying their actions using machine learning (ML). This ML-driven approach aims to provide insights into students’ decision-making patterns, identify areas needing improvement, and adaptively enhance training by tailoring feedback to strengthen cybersecurity skills.