  Neuromorphic systems, inspired by the complexity and functionality of the
human brain, have gained interest in academic and industrial attention due to
their unparalleled potential across a wide range of applications. While their
capabilities herald innovation, it is imperative to underscore that these
computational paradigms, analogous to their traditional counterparts, are not
impervious to security threats. Although the exploration of neuromorphic
methodologies for image and video processing has been rigorously pursued, the
realm of neuromorphic audio processing remains in its early stages. Our results
highlight the robustness and precision of our FPGA-based neuromorphic system.
Specifically, our system showcases a commendable balance between desired signal
and background noise, efficient spike rate encoding, and unparalleled
resilience against adversarial attacks such as FGSM and PGD. A standout feature
of our framework is its detection rate of 94%, which, when compared to other
methodologies, underscores its greater capability in identifying and mitigating
threats within 5.39 dB, a commendable SNR ratio. Furthermore, neuromorphic
computing and hardware security serve many sensor domains in mission-critical
and privacy-preserving applications.
