Spectrum Optimization of Dynamic Networks for Reduction of Vulnerability Against Adversarial Resonance Attacks

TL;DR

This paper proposes spectrum optimization techniques to enhance network robustness against adversarial resonance attacks, reducing vulnerability by adjusting the network's spectral properties based on second order dynamics.

Contribution

It introduces the concept of network vulnerability and develops two spectrum optimization methods to minimize resonance amplitude under adversarial attacks.

Findings

Both methods effectively reduce network vulnerability in numerical simulations.

Spectrum optimization significantly decreases resonance amplitudes under attack.

The approaches are applicable to networks modeled with second order dynamics.

Abstract

Resonance is a well-known phenomenon that happens in systems with second order dynamics. In this paper we address the fundamental question of making a network robust to signal being periodically pumped into it at or near a resonant frequency by an adversarial agent with the aim of saturating the network with the signal. Towards this goal, we develop the notion of network vulnerability, which is measured by the expected resonance amplitude on the network under a stochastically modeled adversarial attack. Assuming a second order dynamics model based on the network graph Laplacian matrix and a known stochastic model for the adversarial attack, we propose two methods for minimizing the network vulnerability through optimization of the spectrum of the network graph. We provide extensive numerical results analyzing the effects of both methods.