Optimization under Attack: Resilience, Vulnerability, and the Path to Collapse

TL;DR

This paper investigates how distributed multi-agent optimization in smart city infrastructures responds to adversarial attacks, revealing the conditions leading from resilience to collapse through extensive real-world data analysis.

Contribution

It provides the first large-scale empirical assessment of multi-agent optimization vulnerability under adversarial influence, offering a benchmark dataset and insights for designing self-healing strategies.

Findings

Optimization remains resilient under certain adversarial conditions

Specific adversary placements significantly increase vulnerability

Collapse occurs when adversarial severity exceeds a critical threshold

Abstract

Optimization is instrumental for improving operations of large-scale socio-technical infrastructures of Smart Cities, for instance, energy and traffic systems. In particular, understanding the performance of multi-agent discrete-choice combinatorial optimization under distributed adversary attacks is a compelling and underexplored problem, since multi-agent systems exhibit a large number of remote control variables that can influence in an unprecedented way the cost-effectiveness of distributed optimization heuristics. This paper unravels for the first time the trajectories of distributed optimization from resilience to vulnerability, and finally to collapse under varying adversary influence. Using real-world data to emulate over 28 billion multi-agent optimization scenarios, we exhaustively assess how the number of agents with different adversarial severity and network positioning influences optimization performance, including the influence on Pareto optimal points. With this novel large-scale dataset, made openly available as a benchmark, we disentangle how optimization remains resilient to adversaries and which adversary conditions are required to make optimization vulnerable or collapsed. These new findings can provide new insights for designing self-healing strategies for fault-tolerance and fault-correction in adversarial distributed optimization that have been missing so far.