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Evolutionary disaster recovery: Minimizing RTO after ransomware attacks using genetic algorithms.

Organizational resilience faces an increasing worldwide threat from ransomware attacks, which disrupt essential infrastructure and result in major financial and operational damage. The incidents create extended system outages, which trigger a chain reaction of disruptions throughout supply chains an...

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Publicado en:Journal of Emergency Management Vol. 24; no. 4; pp. 529 - 539
Autor principal: Landaeta, Eduardo
Formato: research tables/charts Journal Article
Publicado: Weston Medical Publishing, LLC Jul/Aug2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jul/Aug2026
      vid: 24
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      pub: Weston Medical Publishing, LLC
      place: Weston, Massachusetts
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        atl: Evolutionary disaster recovery: Minimizing RTO after ransomware attacks using genetic algorithms.
      aug:
        au: Landaeta, Eduardo
        affil: Faculty Affiliated, Institute for Coastal Adaptation and Resilience, Old Dominion University, Norfolk, Virginia
      sug:
        subj:
          Data Breach Prevention and Control
          Computer Viruses
          Disaster Planning Evaluation
          Data Security Economics
          Risk Management
          Turnaround Time Evaluation
          Genetic Algorithms
          Models, Theoretical
          Human
          Financial Management
          Simulations
          Conceptual Framework
          Data Analysis Software
          Budgets
          Evolution
      ab: Organizational resilience faces an increasing worldwide threat from ransomware attacks, which disrupt essential infrastructure and result in major financial and operational damage. The incidents create extended system outages, which trigger a chain reaction of disruptions throughout supply chains and connected systems. This research develops a genetic algorithm (GA)-based solution to optimize disaster recovery operations after ransomware attacks occur. The model works to achieve three major objectives, which include minimizing recovery time objective (RTO) and restoring vital systems first while optimizing resource distribution within financial limits. The GA uses selection, crossover, and mutation to develop recovery sequences through an iterative process of multiobjective optimization. The simulation analyzed eight enterprise systems, which had different criticality levels and cost and availability characteristics. Simulation results show that the GA-optimized recovery plan performs better than standard and random recovery approaches by cutting down RTO and recovery expenses while enhancing system operational availability. The research demonstrates how evolutionary computing enhances cyber risk management while providing a flexible framework to boost ransomware resistance in advanced digital systems.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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