Network resilience against intelligent attacks constrained by degree dependent node removal cost

TL;DR

This paper analyzes how the cost of removing nodes based on their degree affects the resilience of complex networks against intelligent attacks, revealing that degree-dependent costs significantly influence optimal attack strategies and network robustness.

Contribution

It introduces a model linking node removal costs to attack strategies and demonstrates how degree-dependent costs alter network resilience, especially favoring power law networks under certain conditions.

Findings

Resilience depends strongly on node removal cost functions.

Power law networks are more resilient than Poissonian ones when removal costs increase rapidly with degree.

Optimal attack strategies vary with cost functions and resource constraints.

Abstract

We study the resilience of complex networks against attacks in which nodes are targeted intelligently, but where disabling a node has a cost to the attacker which depends on its degree. Attackers have to meet these costs with limited resources, which constrains their actions. A network's integrity is quantified in terms of the efficacy of the process that it supports. We calculate how the optimal attack strategy and the most attack-resistant network degree statistics depend on the node removal cost function and the attack resources. The resilience of networks against intelligent attacks is found to depend strongly on the node removal cost function faced by the attacker. In particular, if node removal costs increase sufficiently fast with the node degree, power law networks are found to be more resilient than Poissonian ones, even against optimized intelligent attacks.