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ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
PUF Probe: A PUF-based Hardware Authentication Equipment ...
Vishal D. Jadhav, Narahari N. Moudhgalya, Tapabrata Sen, T. V. P · 2023-07-28 · via eess.SP updates on arXiv.org

Intelligent Electronic Devices (IEDs) are vital components in modern electrical substations, collectively responsible for monitoring electrical parameters and performing protective functions. As a result, ensuring the integrity of IEDs is an essential criteria. While standards like IEC 61850 and IEC 60870-5-104 establish cyber-security protocols for secure information exchange in IED-based power systems, the physical integrity of IEDs is often overlooked, leading to a rise in counterfeit and tainted electronic products. This paper proposes a physical unclonable function (PUF)-based device (IEDPUF probe) capable of extracting unique hardware signatures from commercial IEDs. These signatures can serve as identifiers, facilitating the authentication and protection of IEDs against counterfeiting. The paper presents the complete hardware architecture of the IEDPUF probe, along with algorithms for signature extraction and authentication. The process involves the central computer system (CCS) initiating IED authentication requests by sending random challenges to the IEDPUF probe. Based on the challenges, the IEDPUF probe generates responses, which are then verified by the CCS to authenticate the IED. Additionally, a two-way authentication technique is employed to ensure that only verified requests are granted access for signature extraction. Experimental results confirm the efficacy of the proposed IEDPUF probe. The results demonstrate its ability to provide real-time responses possessing randomness while uniquely identifying the IED under investigation. The proposed IEDPUF probe offers a simple, cost-effective, accurate solution with minimal storage requirements, enhancing the authenticity and integrity of IEDs within electrical substations