The paper investigates blind false data injection attacks (FDIAs) on power grids, showing that for a connected DC branch‑flow model the residual‑sensitive subspace equals the weighted cycle space. This space is both necessary and sufficient for constructing complete stealthy attacks, revealing that only cycle‑space knowledge is required. The authors propose a benchmark, a measurement‑only reconstruction method, and extend the analysis to AC systems via a cycle manifold, demonstrating practical attack generation on GPUs.
By Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami Puzis
arXiv:2606. 08473v1 Announce Type: new Abstract: 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.
By Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami Puzis
arXiv:2606. 06347v1 Announce Type: cross Abstract: This paper addresses the problem of attack detection in cyber-physical systems without any knowledge of the plant model or its structure.
By Sribalaji C. Anand, Anh Tung Nguyen, George J. Pappas
arXiv:2606.
By Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang, Jinyuan Sun, Kevin Tomsovic
arXiv:2509.05259v2 Announce Type: replace
Abstract: Automatic Generation Control (AGC) plays a critical role in maintaining power balance across multi-area power systems. However, its complete relian...
By Ahmad Mohammad Saber, Alok Paranjape, Jehad Jilan, Niranjana Naveen Nambiar, Amr Youssef, Deepa Kundur
arXiv:2606. 03430v1 Announce Type: cross Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which allow adversaries to create evasive traffic offline.
By Maxime Schwarzer, Laurin Holz, Tobias Huerten, Johannes Loevenich, Thies Moehlenhof, Roberto Rigolin F. Lopes, Veit Hagenmeyer
arXiv:2604.22784v2 Announce Type: replace
Abstract: Power System State Estimation (PSSE) converts geographically distributed measurements into the voltage magnitudes and phase angles needed for grid...
By Solon Falas, Markos Asprou, Charalambos Konstantinou, Maria K. Michael
The paper introduces ExCYDER, an explainable AI framework for anomaly detection in Distributed Energy Resource (DER) networks. It combines LightGBM with SHAP to self-verify alerts, ensuring that each detection aligns with feature‑attribution evidence. Experiments on a realistic DNP3 dataset show over 98% detection accuracy, 44.6% rule‑SHAP consistency, 14.5 ms SHAP latency per alert, and minimal confidence deviation, while distinguishing coherent from inconsistent alerts without sacrificing accuracy.
By Damilola Popoola, Souradeep Bhattacharya, Manimaran Govindarasu
The paper introduces a black-box defense strategy for smart meter data that uses a proxy-guided hierarchical reinforcement learning framework to generate battery-based load-shaping policies. These policies inject realistic yet misleading appliance-level signatures into aggregate power signals, disrupting non-intrusive load monitoring attacks. Experiments on UK-DALE and REDD datasets show significant increases in appliance-level reconstruction error and reductions in attacker F1 scores across multiple unseen NILM models.
By Ruichang Zhang, Mustafa A. Mustafa
EvEMTBench is an open, executable, and versioned benchmark designed to standardize the evaluation of machine‑learning methods for power system protection. It defines 12 protection and event‑analysis functions across four grids (20–345 kV) as 24 scored tasks, enabling structured assessment under varied observability, distribution shifts, and cross‑grid transfer scenarios. The benchmark includes committed data partitions, leakage controls, and reproducible reporting, and demonstrates that wider observability does not always help, that shifted conditions expose hidden failures, and that fault detection transfers better than fault localization.
By Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
arXiv:2512. 22179v3 Announce Type: replace Abstract: Detecting previously unseen attacks remains a major challenge for machine learning-based intrusion detection systems.
By Rajeeb Thapa Chhetri, Saurab Thapa, Avinash Kumar, Zhixiong Chen
arXiv:2608. 16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS).
By Konstantinos E. Kampourakis, Vasileios Gkioulos, Sokratis Katsikas