Explainable AI Based Diagnosis of Poisoning Attacks in Evolutionary Swarms

TL;DR

This paper presents an explainable AI framework to diagnose poisoning attacks in evolutionary swarm systems, revealing that over 10% data manipulation leads to inefficient cooperation among autonomous agents.

Contribution

It introduces a novel framework combining explainable AI and evolutionary swarm modeling to detect and analyze data poisoning effects in multi-agent systems.

Findings

Poisoning above 10% causes non-optimal strategies.

Explainable AI quantifies poisoning effects.

Footprint characterizations enable diagnosis.

Abstract

Swarming systems, such as for example multi-drone networks, excel at cooperative tasks like monitoring, surveillance, or disaster assistance in critical environments, where autonomous agents make decentralized decisions in order to fulfill team-level objectives in a robust and efficient manner. Unfortunately, team-level coordinated strategies in the wild are vulnerable to data poisoning attacks, resulting in either inaccurate coordination or adversarial behavior among the agents. To address this challenge, we contribute a framework that investigates the effects of such data poisoning attacks, using explainable AI methods. We model the interaction among agents using evolutionary intelligence, where an optimal coalition strategically emerges to perform coordinated tasks. Then, through a rigorous evaluation, the swarm model is systematically poisoned using data manipulation attacks. We showcase the applicability of explainable AI methods to quantify the effects of poisoning on the team strategy and extract footprint characterizations that enable diagnosing. Our findings indicate that when the model is poisoned above 10%, non-optimal strategies resulting in inefficient cooperation can be identified.