MARCIM-WG: A cyber wargame proposal based on math modeling applied in a naval scenario
- URL: http://arxiv.org/abs/2606.12395v1
- Date: Wed, 10 Jun 2026 17:56:53 GMT
- Title: MARCIM-WG: A cyber wargame proposal based on math modeling applied in a naval scenario
- Abstract summary: This paper introduces MARCIM-WG, a learning-oriented maritime cyberdefense wargame designed following the NATO wargaming methodology.<n>The proposal is specified through High-Level Design (HLD) and Low-Level Design (LLD) specifications and instantiated in a fictional maritime cyber crisis scenario.<n>Results show a +34.0 percentage-point improvement in the intervention group, with the largest gains in comprehension-related competencies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- From Security Events to Conflict States: A Three-layer Cyber Defense Scenario Model for Enhanced Cyber Situational Awareness [3.453412136204441]
This paper presents a mission-oriented cyber-defense framework for Cyber Situational Awareness (CSA) and decision support.<n>The framework is instantiated in a NetLogo agent-based simulation of an operational environment structured across the Tactical Edge Zone (TEZ), Mission Operations Zone (MOZ), and Enterprise Support Zone (ESZ)<n>Results indicate that the framework and its implementation preserve coherent relationships between attack progression, telemetry-driven uncertainty management, mission-impact assessment, and cost-aware defensive decision support guided by mission-risk prioritization.
arXiv Detail & Related papers (2026-08-27T14:56:33Z) - Provably Secure Agent Guardrail [89.79561918065122]
Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
arXiv Detail & Related papers (2026-05-28T02:12:41Z) - Hybrid Tabletop Exercise (TTX) based on a Mathematical Simulation-based Model for the Maritime Sector [0.0]
There is a need for targeted cybersecurity education to enhance executive decision-making and crisis response.<n>Traditional training methods, such as cyber wargames and Tabletop Exercises (TTX), aim to develop abilities to face critical incidents.<n>This paper presents a novel approach to cybersecurity and cyberdefense education through the design of a specialized hybrid TTX for the maritime domain.
arXiv Detail & Related papers (2026-02-17T19:49:18Z) - Agentic AI Reasoning for Mobile Edge General Intelligence: Fundamentals, Approaches, and Directions [74.35421055079655]
Large language models (LLMs) have enabled an emergence of agentic artificial intelligence (AI) with powerful reasoning and autonomous decision-making capabilities.<n>Mobile Edge General Intelligence (MEGI) brings real-time, privacy-preserving reasoning to the network edge.<n>We propose a joint optimization framework for efficient LLM reasoning deployment in MEGI.
arXiv Detail & Related papers (2025-09-27T10:53:48Z) - Bayesian and Multi-Objective Decision Support for Real-Time Cyber-Physical Incident Mitigation [6.852472228194646]
This research proposes a real-time, adaptive decision-support framework for mitigating cyber incidents in cyber-physical systems.<n>It is developed in response to an increasing reliance on these systems within critical infrastructure and evolving adversarial tactics.
arXiv Detail & Related papers (2025-08-31T09:47:38Z) - CyGATE: Game-Theoretic Cyber Attack-Defense Engine for Patch Strategy Optimization [73.13843039509386]
This paper presents CyGATE, a game-theoretic framework modeling attacker-defender interactions.<n>CyGATE frames cyber conflicts as a partially observable game (POSG) across Cyber Kill Chain stages.<n>The framework's flexible architecture enables extension to multi-agent scenarios.
arXiv Detail & Related papers (2025-08-01T09:53:06Z) - Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion [56.566914768257035]
We present Adversarial Object Fusion (AdvOF), a novel attack framework targeting vision-and-language navigation (VLN) agents in service-oriented environments.<n>We show AdvOF can effectively degrade agent performance under adversarial conditions while maintaining minimal interference with normal navigation tasks.<n>This work advances the understanding of service security in VLM-powered navigation systems, providing computational foundations for robust service composition in physical-world deployments.
arXiv Detail & Related papers (2025-05-29T09:14:50Z) - Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture [68.83934802584899]
We propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC)<n>FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering.<n> Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.
arXiv Detail & Related papers (2025-03-24T01:56:30Z) - Threat-Informed Cyber Resilience Index: A Probabilistic Quantitative Approach to Measure Defence Effectiveness Against Cyber Attacks [0.36832029288386137]
This paper introduces the Cyber Resilience Index (CRI), a threat-informed probabilistic approach to quantifying an organisation's defence effectiveness against cyber-attacks (campaigns)
Building upon the Threat-Intelligence Based Security Assessment (TIBSA) methodology, we present a mathematical model that translates complex threat intelligence into an actionable, unified metric similar to a stock market index, that executives can understand and interact with while teams can act upon.
arXiv Detail & Related papers (2024-06-27T17:51:48Z) - Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare [16.378537388284027]
We are currently facing unprecedented cyber warfare with the rapid evolution of tactics, increasing asymmetry of intelligence, and the growing accessibility of hacking tools.
This chapter aims to highlight the pivotal role of game-theoretic models and foundation models (FMs) in analyzing, designing, and implementing cyber deception tactics.
arXiv Detail & Related papers (2024-03-14T20:17:57Z) - Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic
Segmentation [79.42338812621874]
Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations.
We formulate a general adversarial training procedure that can perform decently on both adversarial and clean samples.
We propose a dynamic divide-and-conquer adversarial training (DDC-AT) strategy to enhance the defense effect.
arXiv Detail & Related papers (2020-03-14T05:06:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.