Characterizing Network's Most Vulnerable Points Against Adversarial Attacks

• Collaborators: Amirhossein Nazerian, Malbor Asllani, David Phillips, Hernan Makse, Francesco Sorrentino
• July 2024 - November 2025

Consensus, synchronization, formation control, and power grid balance are all examples of virtuous dynamical states that may arise in networks. In this research, we focused on how such states can be destabilized from a fundamental perspective; namely, we aimed to address the question of how one or a few intruder agents within an otherwise functioning network may compromise its dynamics.

We showed that a single adversarial node coupled via adversarial connections to one or more other nodes is sufficient to destabilize the entire network, which we prove to be more efficient than targeting multiple nodes. We also showed that concentrating the attack on a single low-indegree node induces the greatest instability, challenging the common assumption that hubs are the most critical nodes. This leads to a new characterization of the vulnerability of a node, which contrasts with previous work, and identifies low-indegree nodes (as opposed to the hubs) as the most vulnerable components of a network. Our results are derived for linear systems but hold true for nonlinear networks, including those described by the Kuramoto model. Finally, we derive scaling laws showing that larger networks are less susceptible, on average, to single-node attacks. Overall, these findings highlight an intrinsic vulnerability of technological systems, such as autonomous networks, sensor networks, power grids, and the internet of things, with implications that extend also to the realm of complex social and biological networks.