Cyber-Physical Systems

Also known as CPS

Cyber-physical systems connect sensors, software, networks, and controllers to physical processes, so computational decisions can affect safety and real-world operation. Researchers use model-based control and digital twins alongside formal methods such as Signal Temporal Logic verification, and data-driven approaches including reinforcement learning and graph-based anomaly detection. These methods are applied to settings such as industrial control systems, power grids, autonomous robots, and connected vehicles, where they must handle faults, cyberattacks, changing conditions, and tight timing constraints. A central challenge is assuring safety and security when learned or language-model-based components are difficult to verify, while testing them under realistic physical and network conditions.

Latest papers

85 papers9 papers in the last four weeks, down 25% on the four weeks before. 0.1% of all new papers.

GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
May 15, 2026Mohammad A. Razzaque, Muta Tah HiraCyber-Physical Systems Physics-Informed Generative Modeling
School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK
Quantifying Cyber-Vulnerability in Power Electronics Systems via an Impedance-Based Attack Reachable Domain
May 14, 2026Hongwei Zhen, Ze Yu, Xin Xiang +2Cyber-Physical Systems Reachability Analysis
Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems
May 13, 2026MD Shafikul Islam, Jordan CardenCyber-Physical Systems Autoencoders
Benchmarking Sensor-Fault Robustness in Forecasting
May 11, 2026Alexander Windmann, Philipp Wittenberg, Gianluca Manca +3Cyber-Physical Systems Time Series Forecasting
1Helmut Schmidt University · 2Ruhr University Bochum · 3ABB Corporate Research +2
PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs
Apr 30, 2026Raviteja Bommireddy, Varshith Bandaru, Lohith Pakala +1Industrial Anomaly Detection Anomaly Detection
Department of CSE IIITDM Kancheepuram Chennai, India
SMSI: System Model Security Inference: Automated Threat Modeling for Cyber-Physical Systems
Apr 26, 2026RoÝah Radaideh, Ali KhreisCyber-Physical Systems Cybersecurity
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada
Grammar-Constrained Refinement of Safety Operational Rules Using Language in the Loop: What Could Go Wrong
Apr 26, 2026Khouloud Gaaloul, Zaid Ghazal, Madhu Latha Pulimi +1Cyber-Physical Systems Safety-Critical Control
University of Michigan–Dearborn Dearborn, MI, USA
Architectures for Robust Self-Organizing Energy Systems under Information and Control Constraints
Apr 23, 2026Emilie Frost, Astrid NießeCyber-Physical Systems Robust Control
Department of Computing Science, Carl von Ossietzky Universität Oldenburg, Ammerländer Heerstraße 114-118, Oldenburg, Germany · Distributed Artificial Intelligence, OFFIS - Institute for Information Technology, Escherweg 2, Oldenburg, Germany
ACT: Automated CPS Testing for Open-Source Robotic Platforms
Apr 13, 2026Aditya A. Krishnan, Donghoon Kim, Hokeun KimAutomated Software Testing Cyber-Physical Systems
Arizona State University · Arkansas State University
Agentic Driving Coach: Robustness and Determinism of Agentic AI-Powered Human-in-the-Loop Cyber-Physical Systems
Apr 13, 2026Deeksha Prahlad, Daniel Fan, Hokeun KimHuman-in-the-Loop AI Cyber-Physical Systems
Arizona State University Tempe, AZ, United States
Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks
Apr 13, 2026Axel Andersson, György DánCPS Security Cyber-Physical Systems
Department of Network and Systems Engineering, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden
Towards Counterfactual Explanation and Assertion Inference for CPS Debugging
Apr 9, 2026Zaid Ghazal, Hadiza Yusuf, Khouloud GaaloulCyber-Physical Systems Counterfactual Explanations
University of Michigan-Dearborn, Dearborn, MI, USA
Constraint-Driven Warm-Freeze for Efficient Transfer Learning in Photovoltaic Systems
Apr 7, 2026Yasmeen Saeed, Ahmed Sharshar, Mohsen GuizaniFine-Tuning Transfer Learning
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems
Apr 7, 2026Shaofei Huang, Christopher M. Poskitt, Lwin Khin SharCybersecurity CPS Security
Singapore Management University, Singapore
Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry
Mar 31, 2026Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian +1Cybersecurity Internet of Things
Department of Computer Science and Engineering, University of North Texas (UNT) · Versaterm Public Safety Inc., Canada · High Performance Cloud Computing (HPCC) Lab, Department of Computer Science and Engineering, University of North Texas (UNT)
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
Mar 26, 2026John Ayotunde, Qinghua Xu, Guancheng Wang +1Class-Imbalanced Learning Cyber-Physical Systems
Lero Research Ireland Centre for Software Research, University of Limerick, Castletroy, Limerick, Ireland. · the University of Ottawa, Canada, and Lero Research Ireland Centre for Software Research, University of Limerick, Ireland.
Synchronous Signal Temporal Logic for Decidable Verification of Cyber-Physical Systems
Mar 26, 2026Partha Roop, Sobhan Chatterjee, Avinash Malik +2Signal Temporal Logic Formal Verification
University of Auckland, Auckland, New Zealand · Auckland University of Technology, Auckland, New Zealand
SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems
Mar 23, 2026Weizhe Xu, Mengyu Liu, Fanxin KongLLM Safety Cyber-Physical Systems
University of Notre Dame, Notre Dame, IN, USA · Washington State University, Richland, WA, USA
From Passive Monitoring to Active Defence: Resilient Control of Manipulators Under Cyberattacks
Mar 13, 2026Gabriele Gualandi, Alessandro V. PapadopoulosCyber-Physical Systems False Data Injection Attacks
Department of Computer Science and Engineering, Mälardalen University, Västerås, Sweden
Flowcean - Model Learning for Cyber-Physical Systems
Mar 12, 2026Maximilian Schmidt, Swantje Plambeck, Markus Knitt +4Cyber-Physical Systems System Identification
Hamburg University of Technology, Hamburg, Germany · Fraunhofer — Center for Maritime Logistics and Services CML, Hamburg, Germany · OFFIS — Institute for Information Technology, Oldenburg, Germany
Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention
Mar 11, 2026Kosti Koistinen, Kirsi Hellsten, Joni Herttuainen +1Industrial Anomaly Detection Industrial Control System Security
Aalto University School of Science Computer Science Department P.O.Box 11000, 00076 AALTO, Finland
LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy
Feb 19, 2026Hsin-Jung Yang, Zhanhong Jiang, Prajwal Koirala +3Reinforcement Learning Safe RL
Dept. of Mechanical Engineering Iowa State University Ames, USA · Translational AI Center Iowa State University Ames, USA · Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca, USA
Cloud Is Closer Than It Appears: Revisiting the Tradeoffs of Distributed Real-Time Inference
Feb 17, 2026Pragya Sharma, Hang Qiu, Mani SrivastavaDistributed Inference Cyber-Physical Systems
University of California Los Angeles · University of California Riverside
Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems
Jan 30, 2026H. Sinan Bank, Daniel R. HerberCyber-Physical Systems Retrieval-Augmented Generation
Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA
PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning
Mar 9, 2024Mojtaba Taherisadr, Salma ElmalakiRL Control Cyber-Physical Systems
University of California, Irvine, USA

May 15, 2026J/K move · Enter open · S save

GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks

Mohammad A. Razzaque, Muta Tah Hira

School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK

Cyber-Physical Systems Physics-Informed Generative Modeling False Data Injection Attacks Synthetic Data Generation Cybersecurity Physics-Informed ML+1

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Training and evaluating false data injection attack (FDIA) detectors for power systems is constrained by data scarcity. Operational grid measurements are commercially sensitive, and hand-crafted attacks fail to capture complex distributional structures imposed by network physics. We present GenAI-FDIA, a framework benchmarking a pool of

P{=}20

architectures for physics-compliant FDIA synthesis, spanning Wasserstein GANs, MMD-VAEs, normalising flows, diffusion models, and cross-family hybrids. These are evaluated across three IEEE testbeds (14-bus DC, 30-bus DC, and 14-bus AC) under a 60/20/20 chronological split using data-driven Bad Data Detection (BDD) threshold calibration. Our empirical results verify that these models generate high-fidelity attacks, with all architectures achieving evasion rates of

ε_{\text{BDD}} \ge 86.6\%

on the 14-bus network; additionally, limiting an attacker's topological knowledge induces a measurable degradation in stealthiness (

p \le 0.0022

). Crucially, we identify a previously unreported failure mode: applying affine physics projections directly in normalised feature spaces critically displaces the attack vector, collapsing BDD evasion from

{\sim}55\%

<\!2\%

on the 30-bus testbed. We resolve this via a novel inference-time harmoniser, restoring full stealthiness (

ε_{\text{BDD}}{=}100\%

) across all physics-informed variants without retraining. Finally, we isolate a covariance-collapse phenomenon (

κ\approx {-}0.076

) within advanced hybrid architectures and rectify it through 50-epoch warm-up schedules (

κ\to 0.785

Δ\text{MMD}={-}3.1\%

). Ultimately, GenAI-FDIA delivers a robust recovery blueprint applicable to any physics-constrained generative model deployed for power-system security.