Resonance Reduction Against Adversarial Attacks in Dynamic Networks via Eigenspectrum Optimization

Autor:	Sahin, Alp, Kozachuk, Nicolas, Blum, Rick S., Bhattacharya, Subhrajit
Rok vydání:	2024
Předmět:	Computer Science - Social and Information Networks Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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 that leverage the principle of eigenspectrum optimization. We provide extensive numerical results analyzing the effects of both methods. Comment: 13 pages, 18 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.00126 Zobrazit plný text záznamu View this record from Arxiv