Adapting Noise-Driven PUF and AI for Secure WBG ICS: A Proof-of-Concept Study

TL;DR

This study presents a novel approach combining noise-driven PUFs and AI-based anomaly detection to enhance cybersecurity in WBG-based industrial control systems, demonstrating high accuracy and low latency in simulations.

Contribution

It introduces a dual-use noise exploitation framework that unites hardware authentication with real-time threat detection in WBG ICS environments.

Findings

Achieved 95% detection accuracy in simulations.

Demonstrated sub-millisecond processing latency.

Validated robustness against EMI and cyber-physical attacks.

Abstract

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.