Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS). This paper studies the robustness of SNNs against adversarial attacks on such DVS-based systems, and proposes R-SNN, a novel methodology for robustifying SNNs through efficient DVS-noise filtering. We are the first to generate adversarial attacks on DVS signals (i.e., frames of events in the spatio-temporal domain) and to apply noise filters for DVS sensors in the quest for defending against adversarial attacks. Our results show that the noise filters effectively prevent the SNNs from being fooled. The SNNs in our experiments provide more than 90% accuracy on the DVS-Gesture and NMNIST datasets under different adversarial threat models.