More Effective Centrality-Based Attacks on Weighted Networks

TL;DR

This paper introduces flow-based centrality metrics for targeted node attacks on weighted networks, demonstrating their superior effectiveness over traditional shortest-path based methods through extensive experiments.

Contribution

It proposes novel flow-based centrality metrics for attack strategies on weighted networks, improving attack effectiveness compared to existing shortest-path based approaches.

Findings

Flow-based attack methods outperform traditional shortest-path based methods.

Three new flow-based centralities are effective in identifying critical nodes.

Experimental results on artificial and real-world networks validate the approach.

Abstract

Only when understanding hackers' tactics, can we thwart their attacks. With this spirit, this paper studies how hackers can effectively launch the so-called 'targeted node attacks', in which iterative attacks are staged on a network, and in each iteration the most important node is removed. In the existing attacks for weighted networks, the node importance is typically measured by the centralities related to shortest-path lengths, and the attack effectiveness is also measured mostly by length-related metrics. However, this paper argues that flows can better reflect network functioning than shortest-path lengths for those networks with carrying traffic as the main functionality. Thus, this paper proposes metrics based on flows for measuring the node importance and the attack effectiveness, respectively. Our node importance metrics include three flow-based centralities (flow betweenness, current-flow betweenness and current-flow closeness), which have not been proposed for use in the attacks on weighted networks yet. Our attack effectiveness metric is a new one proposed by us based on average network flow. Extensive experiments on both artificial and real-world networks show that the attack methods with our three suggested centralities are more effective than the existing attack methods when evaluated under our proposed attack effectiveness metric.