  The security of mobile robotic networks (MRNs) has been an active research
topic in recent years. This paper demonstrates that the observable interaction
process of MRNs under formation control will present increasingly severe
threats. Specifically, we find that an external attack robot, who has only
partial observation over MRNs while not knowing the system dynamics or access,
can learn the interaction rules from observations and utilize them to replace a
target robot, destroying the cooperation performance of MRNs. We call this
novel attack as sneak, which endows the attacker with the intelligence of
learning knowledge and is hard to be tackled by traditional defense techniques.
The key insight is to separately reveal the internal interaction structure
within robots and the external interaction mechanism with the environment, from
the coupled state evolution influenced by the model-unknown rules and
unobservable part of the MRN. To address this issue, we first provide general
interaction process modeling and prove the learnability of the interaction
rules. Then, with the learned rules, we design an Evaluate-Cut-Restore (ECR)
attack strategy considering the partial interaction structure and geometric
pattern. We also establish the sufficient conditions for a successful sneak
with maximum control impacts over the MRN. Extensive simulations illustrate the
feasibility and effectiveness of the proposed attack.
