  Spiking Neural Networks (SNN) are quickly gaining traction as a viable
alternative to Deep Neural Networks (DNN). In comparison to DNNs, SNNs are more
computationally powerful and provide superior energy efficiency. SNNs, while
exciting at first appearance, contain security-sensitive assets (e.g., neuron
threshold voltage) and vulnerabilities (e.g., sensitivity of classification
accuracy to neuron threshold voltage change) that adversaries can exploit. We
investigate global fault injection attacks by employing external power supplies
and laser-induced local power glitches to corrupt crucial training parameters
such as spike amplitude and neuron's membrane threshold potential on SNNs
developed using common analog neurons. We also evaluate the impact of
power-based attacks on individual SNN layers for 0% (i.e., no attack) to 100%
(i.e., whole layer under attack). We investigate the impact of the attacks on
digit classification tasks and find that in the worst-case scenario,
classification accuracy is reduced by 85.65%. We also propose defenses e.g., a
robust current driver design that is immune to power-oriented attacks, improved
circuit sizing of neuron components to reduce/recover the adversarial accuracy
degradation at the cost of negligible area and 25% power overhead. We also
present a dummy neuron-based voltage fault injection detection system with 1%
power and area overhead.
