arXiv Machine Learning June 9, 2026 By Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami Puzis

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

Read the original on arXiv Machine Learning →

arXiv:2605. 28912v2 Announce Type: replace Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 9

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

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

safety

arXiv Machine Learning

Jun 5

Attack Detection using Time Series Foundation Models

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 Machine Learning

Jun 19

Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids

arXiv:2606.

By Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang, Jinyuan Sun, Kevin Tomsovic

safety

arXiv AI

Jun 3

FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems

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

diffusion

arXiv Machine Learning

Aug 3

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection

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

llms diffusion benchmarks

arXiv AI

1d ago

Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies

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

robotics safety

Related stories

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

Attack Detection using Time Series Foundation Models

Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids

FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection

Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies