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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
Design and Detection of Controller Manipulation Attack on...
Siddharth Sankar Acharjee, Arpan Chattopadhyay · 2022-02-21 · via eess.SP updates on arXiv.org

In this paper, we introduce a new attack called controller manipulation attack (CMA) on a Reconfigurable Intelligent Surface (RIS) assisted communication system between a transmitter and a receiver. An attacker has the capability to manipulate the RIS controller and modify the phase shift induced by the RIS elements. The goal of the attacker is to minimize the data rate at the receiver, subject to a constraint on the attack detection probability at the receiver. We consider a number of attack detection models: (i) composite hypothesis testing based attack detection in a given fading block for known channel gains, (ii) sequential quickest detection of CMA in a given fading block for known channel gains, (iii) nonparametric hypothesis test to detect CMA for unknown channel gains over a fading block, and (iv) signal-to-noise-ratio (SNR) moment based detection over possibly multiple fading blocks. In the first case, a simple energy detector turns out to be uniformly most powerful (UMP). In the second case, simplification of the standard CUSUM test and its performance bounds are obtained. In the third case, non-parametric Kolmogorov-Smirnov test is further simplified to a simple per-sample double threshold test. The attack against these three detectors are designed via novel optimization formulations and semidefinite relaxation based solutions. In the fourth case, we consider threshold detection using moments of SNR; various SNR moments under no attack are obtained analytically for large RIS and then used to formulate the attack design problem as a linear program. Finally, numerical results illustrate the performance and trade-offs associated with the attack schemes, and also demonstrate their efficacy.