Xuanwei Lin

About

Xuanwei Lin is a leading researcher at the intersection of neuromorphic computing and cybersecurity, with a primary focus on the robustness of spiking neural networks (SNNs) within Cyber-Physical Systems (CPS). His most cited work, "SPA: An Efficient Adversarial Attack on Spiking Neural Networks using Spike Probabilistic" (2022), represents a groundbreaking contribution to the field. This paper introduces a novel adversarial attack methodology that exploits the probabilistic nature of spike-based communication in SNNs, demonstrating critical vulnerabilities in these biologically inspired networks. The research has profound implications for the secure deployment of SNNs in high-stakes applications such as biometric recognition, AI robotics, autonomous driving, and healthcare—all key technologies for the emerging 6G era. By exposing how malicious inputs can manipulate neural spike patterns, Lin’s work has become essential reading for researchers developing robust neuromorphic systems. His findings have garnered significant attention (5 citations) and are widely referenced in studies on adversarial machine learning and CPS security. Lin’s contributions are pivotal in bridging the gap between advanced AI processing and the safety requirements of next-generation intelligent systems.

Research Focus

Key Achievements

Top Papers

1. 1
   SPA: An Efficient Adversarial Attack on Spiking Neural Networks using Spike Probabilistic

   5 citations · 2022

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