Computing a Best Response against a Maximum Disruption Attack

TL;DR

This paper presents a polynomial-time algorithm for computing a best response in a network formation game under maximum disruption attack, addressing an open problem and extending prior work on attack strategies.

Contribution

It introduces a dynamic programming approach to efficiently compute best responses against maximum disruption adversaries in connected networks.

Findings

Polynomial-time algorithm for best response computation

Algorithm based on dynamic programming with knapsack-like features

Addresses open problem in attack strategy computation

Abstract

Inspired by scenarios where the strategic network design and defense or immunisation are of the central importance, Goyal et al. [3] defined a new Network Formation Game with Attack and Immunisation. The authors showed that despite the presence of attacks, the game has high social welfare properties and even though the equilibrium networks can contain cycles, the number of edges is strongly bounded. Subsequently, Friedrich et al. [10] provided a polynomial time algorithm for computing a best response strategy for the maximum carnage adversary which tries to kill as many nodes as possible, and for the random attack adversary, but they left open the problem for the case of maximum disruption adversary. This adversary attacks the vulnerable region that minimises the post-attack social welfare. In this paper we address our efforts to this question. We can show that computing a best response strategy given a player u and the strategies of all players but u, is polynomial time solvable when the initial network resulting from the given strategies is connected. Our algorithm is based on a dynamic programming and has some reminiscence to the knapsack-problem, although is considerably more complex and involved.