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Targeted attacks on interdependent networks reveal that "dependency-last" strategies are more effective than "dependency-first" or random attacks. Evaluating attack impact requires considering long-term cascading failures, not just immediate damage.

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Area of Science:

  • Network Science
  • Complex Systems
  • Cybersecurity

Background:

  • Engineered network systems rely on component dependencies for efficiency and regulation.
  • These dependencies create vulnerabilities, enabling small failures to propagate and become major risks.
  • Dependent nodes are potential targets for malicious attacks exploiting system interdependencies.

Purpose of the Study:

  • To analyze the effectiveness of different targeted attack strategies on partially interdependent networks.
  • To investigate how dependency strength influences attack outcomes.
  • To understand the dynamics of cascading failures resulting from dependency-based attacks.

Main Methods:

  • Analytical and numerical analysis of percolation transitions in partially interdependent networks.
  • Simulation of attack strategies: "dependency-first" and "dependency-last".
  • Comparison of attack effectiveness based on critical percolation thresholds (p_c) and cascading failure dynamics.

Main Results:

  • "Dependency-first" attacks are less effective than random attacks, showing a lower critical percolation threshold (p_c).
  • "Dependency-last" attacks are more effective than random attacks, resulting in a higher p_c.
  • The long-term impact of cascading failures differs significantly between "dependency-first" and "dependency-last" strategies.

Conclusions:

  • Attack strategy effectiveness in interdependent networks depends on accumulated damage from cascading failures, not just immediate impact.
  • Understanding avalanche dynamics is crucial for designing resilient critical infrastructures against targeted attacks.
  • "Dependency-last" attack strategies pose a greater long-term threat to interdependent networks.