Smart Grid Security and Resilience

Latest papers

• LogiC-Diff: Embedding Security Properties Into AI-Enabled Cyber-Physical Systems

  Ziyan An, John Stankovic, Meiyi Ma · 1 October 2026

  AI-enabled Cyber-Physical Systems (CPS) are highly vulnerable to adversarial and anomalous inputs, where small perturbations can induce cascading errors and unsafe control actions. Existing approaches, such as rule-based filtering, training-time regularization, or diffusion-based reconstruction, eit…

• PEARL: Structural Privacy-Utility Control in Human-Centric CPS via Personalized Early-Exit Deep Reinforcement Learning

  Mojtaba Taherisadr, Salma Elmalaki · 14 September 2026

  In human-centric Cyber-Physical Systems (CPS), personalized Deep Reinforcement Learning (DRL) agents must share fine-grained control actions with cloud services, exposing sensitive private states to inference attacks by honest-but-curious adversaries. Static privacy models fail to address the dynami…

• Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks

  Damilola Popoola, Souradeep Bhattacharya, Manimaran Govindarasu · 14 September 2026

  The rapid growth of Distributed Energy Resources (DERs) has significantly expanded the cyber attack surface of modern power grids. Furthermore, increasing sophistication in attack techniques demands anomaly detection systems (ADS) that are accurate, interpretable, and reliable to support DER cyberse…

• From Cycle Space to Cycle Manifold: Limits and Achievability of Blind False Data Injection Attacks

  Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami Puzis · 11 September 2026

  A false data injection attack (FDIA) can change the estimated grid state while evading a residual-based bad data detector (BDD). Existing blind attacks learn a low-rank measurement subspace, but this algebraic view does not state the physical grid constraints that make an attack stealthy or the mini…

• Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks

  Ammar Kamoona, Sajad Koushkbaghi, Mahdi Jalili, Peter McTaggart, Xinghuo Yu · 10 September 2026

  The digitalisation of electrical distribution networks has increased the exposure of power-grid infrastructure to cyber attacks. Existing intrusion detection systems (IDSs), however, often rely on computationally expensive deep learning models that are difficult to deploy at the edge. Periodic retra…

• Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection

  Branka Stojanovi\'c, Andreas Flatscher, Michael Somma · 9 September 2026

  Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarante…

• Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Models

  Bilal Hussain, Xiao Tang, Qinghe Du, Tan Li, Muhammad Azhar, Danista Khan · 19 August 2026

  Deep learning-based DDoS detectors for 5G-enabled cyber-physical systems face scarce labeled attack data and unrealistic synthetic substitutes, which limit robustness against adaptive adversaries. Detectors trained on hand-crafted attacks with fixed scaling multipliers degrade catastrophically (F1-s…

• Digital Twin-Based Intrusion Detection for Vehicle Powertrain CAN Bus Systems

  Araf Rahman, M Sabbir Salek, Mashrur Chowdhury · 19 August 2026

  Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency, or sequencing and cannot detect attacks that preserve these properties while manipulating the payload. Digital twins (DTs) have been used to emulate…

• Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies

  Konstantinos E. Kampourakis, Vasileios Gkioulos, Sokratis Katsikas · 18 August 2026

  Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS). However, in adversarial environments, the fidelity of a DT cannot be assumed. Communication delays, data manipulation, sensor degradation, or partial information loss may cause the DT state to diverge from…

• Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do

  Md Rezwanul Islam · 18 August 2026

  Machine-learning detectors for power-system cyberattacks are themselves attack surfaces, and quantum machine learning has been proposed for them. We benchmark fidelity-kernel SVMs and variational classifiers against six tuned classical models on public power-system attack data (Mississippi State/ORN…

• TwinGridShield: Consequence-Aware Runtime Authorization for LLM Grid-Agent Actions

  Md Fazley Rafy · 18 August 2026

  Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but syntactic validity does not imply physical admissibility. This paper presents TwinGridShield, a model-independent runtime authorization layer that evaluates each prop…

• Benchmarking Cyberattack Detection in Electric Vehicle Charging Infrastructure with Benign User Updates

  Hannan Chen, Roshni Anna Jacob, Jie Zhang · 13 August 2026

  Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and departure time. Charging manipulation attacks can exploit the same interface and variables; therefore, detecting a request change alone does not establish …

• DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers

  Navid Aftabi, Abhishek Hanchate, Satish Bukkapatnam, Dan Li · 24 July 2026

  Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manipulate actuators. Dynamic watermarking can reveal such tampering, but current schemes assume linear-Gaussian dynamics and use constant watermark statistics, makin…

• BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors

  Chia-Yi Hsu, Yu-Lin Tsai, Yu Zhe, Yan-Lun Chen, Chih-Hsun Lin, Chia-Mu Yu, Yang Zhang, Chun-Ying Huang, Jun Sakuma · 21 July 2026

  Task arithmetic in large-scale pre-trained models enables agile adaptation to diverse downstream tasks without extensive retraining. By leveraging task vectors (TVs), users can perform modular updates through simple arithmetic operations like addition and subtraction. Yet, this flexibility presents …

• Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?

  Clemens Kortmann, Eike Cramer · 9 July 2026

  Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy systems literature, adversarial attacks have been studied with a focus on problems regarding the electricity grid. Such problems include forecasting and …

• A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

  Sajib Debnath, Remon Das · 25 June 2026

  The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels. Curren…

• Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

  Hugo O. Garc\'es, Alejandro J. Rojas, Bernardo A. Hern\'andez, Andr\'es Escalona, Jonathan M. Palma, Md. Rezwan Parvez, Bhushan Gopaluni, Sirish L. Shah · 18 June 2026

  This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks. Four RL reward types are analyzed for accuracy, cost, and resilience. Results show that the Lyapunov reward offers the best resilience…

• Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks

  Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami Puzis · 9 June 2026

  False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when the injected signals align with the pseudo-null space of the system model. Existing model- and data-driven detectors may fail to identify such low-m…

• IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems

  Yuchen Zhang, Ning Xi, Pengbin Feng, Shigang Liu, Jianfeng Ma, Yulong Shen, Yanan Sun, Xiaolin Zhou · 2 June 2026

  Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents. However, existing tools exhibit limited effectiveness in real-time anomaly detection due to the complex dependencies among sensors and actuators. To…

• Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems

  MD Shafikul Islam, Jordan Carden · 2 June 2026

  As Industry 4.0 accelerates the integration of Cyber-Physical Systems (CPS) in manufacturing, robust anomaly detection has become critical for ensuring process safety and security. Current data-driven approaches typically employ "product-agnostic" or global models trained on the aggregate of all nor…

• Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems

  Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami Puzis · 29 May 2026

  The rapid growth of AI-driven data centers and large-scale energy storage systems is increasing the reliance of power system operation on real-time measurement data and automated decision-making. However, many existing detection methods rely on statistical or data-driven analysis of measurements and…

• Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems

  Abile Jean, Kuniyilh S · 28 May 2026

  Cyber-Physical Systems (CPS) integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, industrial automation, and control systems. In the electrical utility domain, various controllers are used in CPS to ensure the system detects and recove…

• ASTRO: Adaptive Spatio-Temporal Reinforcement Optimization for GNN Powered Anomly Detection in Cyber Physical Systems

  Rai Ali Yar, Umaisa Lail, Anwar Shah · 26 May 2026

  Anomaly detection in Industrial Internet of Things (IIoT) environments is essential to protect the Industrial Control Systems (ICS) and Cyber-Physical Systems (CPS) from occuring run time false data injection and other malicious attacks. The increasing complexity of sensor networks and interconnecte…

• Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs

  Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane · 21 May 2026

  Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving tasks such as anomaly detection and capacity estimation. However, most existing frameworks rely on centralized aggregat…

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

  Mohammad A. Razzaque, Muta Tah Hira · 20 May 2026

  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 \textsc{…