🤖 AI Summary
This study addresses the vulnerability of sensor data in industrial control systems to faults and supply chain attacks, exacerbated by the absence of an intrinsic trust mechanism at the measurement layer. To overcome this, the authors propose a process-aware, vendor-agnostic security architecture that embeds a Physical Unclonable Function (PUF) as a hardware root of trust directly within the sensor measurement layer. By integrating voltage-based fingerprinting with a time-based authentication mechanism, the framework enables real-time verification of sensor readings without requiring modifications to existing systems or compromising compatibility with standard industrial control architectures. Validation via a Simulink-based hardware-in-the-loop (HIL) platform demonstrates 99.97% authentication accuracy over 5.18 hours of operation and successful detection of all injected anomalies, including spike faults, hard overloads, and hardware trojans designed to induce unsafe system states.
📝 Abstract
Industrial Control Systems (ICS) rely on sensor feedback to keep safety-critical processes within operational limits. This research presents a hardware-root-of-trust that embeds a Physically Unclonable Function (PUF) at the measurement layer to authenticate sensor readings. The architecture combines voltage fingerprinting with a temporal authentication that integrates with standard industrial control system architecture. The research prototypes the PUF integration on a hardware-in-the-loop (HIL) water tank testbed using a Simulink-based PUF emulator. The system maintains 99.97% accuracy over a 5.18-hour period of normal operation and flags all injected anomalies, including spike faults, hard-over faults, and hardware trojan scenarios that push the system over to an unsafe operational state. The proposed architecture provides a process-aware, vendor-agnostic approach that can integrate with legacy plants to detect sensor signal degradation or sophisticated supply chain attacks.