Wide-bandgap (WBG) technologies offer unprecedented improvements in power system efficiency, size, and performance, but also introduce unique sensor corruption and cybersecurity risks in industrial control systems (ICS), particularly due to high-frequency noise and sophisticated cyber-physical threats. This proof-of-concept (PoC) study demonstrates the adaptation of a noise-driven physically unclonable function (PUF) and machine learning (ML)-assisted anomaly detection framework to the demanding environment of WBG-based ICS sensor pathways. By extracting entropy from unavoidable WBG switching noise (up to 100 kHz) as a PUF source, and simultaneously using this noise as a real-time threat indicator, the proposed system unites hardware-level authentication and anomaly detection. Our approach integrates hybrid machine learning (ML) models with adaptive Bayesian filtering, providing robust and low-latency detection capabilities resilient to both natural electromagnetic interference (EMI) and active adversarial manipulation. Through detailed simulations of WBG modules under benign and attack scenarios--including EMI injection, signal tampering, and node impersonation--we achieve 95% detection accuracy and sub-millisecond processing latency. These results demonstrate the feasibility of physics-driven, dual-use noise exploitation as a scalable ICS defense primitive. Our findings lay the groundwork for next-generation security strategies that leverage inherent device characteristics, bridging hardware and artificial intelligence (AI) for enhanced protection of critical ICS infrastructure.

翻译：宽禁带（WBG）技术为电力系统效率、尺寸和性能带来前所未有的提升，但同时也因其高频噪声和复杂的网络物理威胁，在工业控制系统（ICS）中引入了独特的传感器数据损坏与网络安全风险。本研究通过概念验证（PoC）展示了将噪声驱动的物理不可克隆函数（PUF）与机器学习（ML）辅助异常检测框架适配于WBG基ICS传感器通路的严苛环境。通过从不可避免的WBG开关噪声（高达100 kHz）中提取熵作为PUF源，并同时利用该噪声作为实时威胁指标，所提出的系统实现了硬件级身份认证与异常检测的融合。我们的方法将混合机器学习（ML）模型与自适应贝叶斯滤波相结合，提供了对自然电磁干扰（EMI）和主动对抗性操纵均具有鲁棒性的低延迟检测能力。通过对良性及攻击场景（包括EMI注入、信号篡改和节点伪装）下WBG模块的详细仿真，我们实现了95%的检测精度与亚毫秒级处理延迟。这些结果证明了以物理驱动的双用途噪声利用作为可扩展ICS防御原语的可行性。我们的研究为利用固有器件特性、融合硬件与人工智能（AI）以增强关键ICS基础设施保护的下一代安全策略奠定了基础。