🤖 AI Summary
This study addresses the critical vulnerability of maritime operations to cyber threats due to their heavy reliance on interconnected digital systems, emphasizing the urgent need to enhance decision-makers’ cyber situational awareness and incident response capabilities. To this end, the work proposes an innovative hybrid training system that integrates a physical wargaming board—featuring tokens and cards—with a simulation model driven by mathematical modeling–based computational adjudication. Structured crisis scenarios incorporate friction, resource constraints, and quantifiable consequences through high- and low-level design specifications. Evaluated via a tripartite validation framework (pessimistic, neutral, optimistic), the intervention group demonstrated a statistically significant 34.0-percentage-point improvement in cyber situational awareness, with particularly pronounced gains in comprehension-related competencies, thereby validating both the training efficacy and methodological novelty of the proposed approach.
📝 Abstract
As maritime operations increasingly depend on interconnected digital ecosystems, cyber incidents can propagate across maritime networks and degrade critical services. Strengthening strategic Cyber Situational Awareness (CSA) therefore requires training mechanisms that expose decision-makers to evolving attack dynamics, constrained resources, and the need to align actions with incident-response procedures. This paper introduces MARCIM-WG, a learning-oriented maritime cyberdefense wargame designed following the NATO wargaming methodology and implemented as a hybrid tabletop experience combining a physical board (tokens, indicators, and special cards) with analytically-assisted adjudication supported by a computational simulation model. The proposal is specified through High-Level Design (HLD) and Low-Level Design (LLD) specifications and instantiated in a fictional maritime cyber crisis scenario to enable structured decision cycles, friction, and measurable consequences. Validation combines (i) an operational scenario-based assessment under three configurations (pessimistic, neutral/most likely, optimistic) to verify decision sensitivity and outcome coherence, and (ii) a CSA competency and learning-outcome evaluation using a comparative design against an equivalent control group. Results show a +34.0 percentage-point improvement in the intervention group, with the largest gains in comprehension-related competencies.