Explainable AI Based Diagnosis of Poisoning Attacks in Evolutionary Swarms
This addresses security vulnerabilities in critical applications like multi-drone networks, but it is incremental as it applies existing explainable AI methods to a specific domain.
The paper tackles the problem of data poisoning attacks in evolutionary swarms, which cause inaccurate coordination or adversarial behavior, and uses explainable AI methods to diagnose these attacks, finding that poisoning above 10% leads to non-optimal strategies and inefficient cooperation.
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.