Network Structural Vulnerability: A Multiobjective Attacker Perspective

Abstract

In this paper we provide a novel framework to assess the vulnerability/robustness of a network with respect to pairwise nodes' connectivity. In particular, we consider attackers that aim, at the same time, at dealing the maximum possible damage to the network in terms of the residual connectivity after the attack and at keeping the cost of the attack (e.g., the number of attacked nodes) at a minimum. Differently from previous literature, we consider the attacker perspective using a multiobjective formulation and, rather than making hypotheses on the mindset of the attacker in terms of a particular tradeoff between the objectives, we consider the entire Pareto front of non-dominated solutions. Based on that, we define novel global and local robustness/vulnerability indicators and we show that such indices can be the base for the implementation of effective protection strategies. Specifically, we propose two different problem formulations and we assess their performances. We conclude the paper by analyzing, as case studies, the IEEE 118 power network and the US airline network as it was in 1997, comparing the proposed approach against centrality measures.

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

“…In order to select fewer points for larger destruction to the graph, both the number of selected points and the pairwise connectivity in the remaining graph should be minimized. As a result, the critical node detection problem is also a subset selection MOP, and has been tackled by some MOEAs [40], [41].…”

A Strategy to Improve Infrastructure Survivability via Prioritizing Critical Nodes Protection

“…Finally, assumptions about the attacker preferences are also required in the formulation presented in [10]. In general, centrality measures, such as the node degree or betweenness centrality are often adopted as criticality measures, while in [11] the authors propose a critical index for the elements of a CI by analyzing the solutions of a multi objective optimization problem without any assumption about the attacker behaviour. However, the adoption of a unique metric or indicator about the criticality rate of each node of the system is quite unrealistic due to the complex nature of the infrastructures.…”

“…In literature, graph centrality measures are often adopted as criticality measures for infrastructure but these approaches (e.g. Node degree or node betweenness) are quite ineffective as proved in [11]. In the second stage (Section 3) a methodology to merge multiple criticality metrics, based on the well-known Analytic Hierarchy Process [14], is described in order to overcome the limit about the application of a single metric in a complex environment.…”

“…As described in [11], in general, a multi-objective problem is characterized by the presence of multiple optimal solutions x j ðÞ collected in the Pareto front set P. Each solution is associated to a couple of values f 1 x j ðÞ ÀÁ…”

Aggregating Centrality Rankings: A Novel Approach to Detect Critical Infrastructure Vulnerabilities

“…This is particularly relevant for critical infrastructure networks (e.g., power networks, railway networks, etc. ), where the importance/criticality of a subsystem may not depend just on the physical characteristics of such subsystems, but also on the complex web of connections and relations that intertwine such composing elements [4,5]. Assessing critical infrastructure vulnerabilities is paramount to arrange efficient plans for their protection.…”