Topological security assessment of technological networks

TL;DR

This paper proposes a spectral graph theory-based approach to assess the security of technological networks by identifying critical nodes to prevent malware spread, introducing the AV11 algorithm which outperforms traditional centrality measures.

Contribution

It introduces the AV11 algorithm for topological security assessment, demonstrating its superior performance over existing centrality-based methods in identifying influential nodes.

Findings

AV11 outperforms degree, closeness, betweenness, and dynamical importance measures.

Spectral graph theory provides effective tools for malware propagation analysis.

Identifying critical nodes helps in designing better defense strategies.

Abstract

The spreading of dangerous malware or faults in inter-dependent networks of electronics devices has raised deep concern, because from the ICT networks infections may propagate to other Critical Infrastructures producing the well-known domino or cascading effect. Researchers are attempting to develop a high level analysis of malware propagation discarding software details, in order to generalize to the maximum extent the defensive strategies. For example, it has been suggested that the maximum eigenvalue of the network adjacency matrix could act as a threshold for the malware's spreading. This leads naturally to use the spectral graph theory to identify the most critical and influential nodes in technological networks. Many well-known graph parameters have been studied in the past years to accomplish the task. Here we test our AV11 algorithm showing that outperforms degree, closeness, betweenness centrality and the dynamical importance