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<p>The spread of disinformation poses a global threat, influencing public trust and destabilizing critical systems. While existing studies focus on direct network attacks, the indirect impact of disinformation on system vulnerabilities remains underexplored. This work addresses this gap by proposing a multi-objective decision model to develop strategies for either amplifying or mitigating the effects of disinformation. Using a multi-layer framework, we model an information layer, where disinformation spreads via a susceptible-infected-recovered (SIR) process (simulated through a cellular automata), and a physical layer, where system performance is evaluated through a defined function (e.g., max flow). From the attacker’s perspective, we introduce a tri-objective model to optimize node selection for disinformation injection and inter-layer links to minimize attack costs, reduce network flow, and accelerate disruption. A case study combining a social network and an electric power grid demonstrates the approach’s effectiveness in identifying trade-offs and decision-making strategies.</p>

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Multi-objective model to protect infrastructure networks from disinformation diffusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月25日

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  • Claudio M. Rocco,
  • Kash Barker,
  • Sridhar Radhakrishnan,
  • Jose E. Ramirez-Marquez

摘要

The spread of disinformation poses a global threat, influencing public trust and destabilizing critical systems. While existing studies focus on direct network attacks, the indirect impact of disinformation on system vulnerabilities remains underexplored. This work addresses this gap by proposing a multi-objective decision model to develop strategies for either amplifying or mitigating the effects of disinformation. Using a multi-layer framework, we model an information layer, where disinformation spreads via a susceptible-infected-recovered (SIR) process (simulated through a cellular automata), and a physical layer, where system performance is evaluated through a defined function (e.g., max flow). From the attacker’s perspective, we introduce a tri-objective model to optimize node selection for disinformation injection and inter-layer links to minimize attack costs, reduce network flow, and accelerate disruption. A case study combining a social network and an electric power grid demonstrates the approach’s effectiveness in identifying trade-offs and decision-making strategies.