Intrusion detection for stochastic task allocation in robot swarms

Abstract

We present a novel framework for integrity analysis of swarm robotic systems using the symmetric Kullback-Leibler Divergence. The objective is to understand a robot swarm's vulnerability to malicious intrusion and to develop the necessary computational tools that would detect the presence of malicious agents within the swarm. Using ensemble approaches for modeling and analyzing stochastic task allocation, we analyze the performance of the proposed strategy subject to different system parameters, and show how different design choices can facilitate early intrusion detection. We further evaluate the performance of our method in realistic scenarios through stochastic simulations for different team sizes. The main contribution is an analysis framework whose output can be used to avoid system-inherent design flaws and to decrease the damage that can be inflicted by an undetected attacker.