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

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

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arXiv:2606. 01691v1 Announce Type: cross Abstract: Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 10

Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attention

arXiv:2603. 10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational technology and networked environments.

By Kosti Koistinen, Kirsi Hellsten, Joni Herttuainen, Kimmo K. Kaski

arXiv Machine Learning

Jul 28

Domain-Prior-Regularized Graph Modeling for Anomaly Detection in Cyber-Physical Systems

arXiv:2607. 23197v1 Announce Type: new Abstract: Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations from normal behavior can indicate process disruption.

By Youngseok Hwang, Joonsung Kwon, Geonwoo Lee, Hyunwoo Park

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Jul 20

Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes

arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.

By Jaeyeong Lee, Taeseong Yoon, Wonmo Koo, Heeyoung Kim

arXiv AI

Aug 11

An Explainable GNN Framework for Component-Level Anomaly Diagnosis

arXiv:2608. 09246v1 Announce Type: new Abstract: Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS).

By Sena Ozgunay (IMT, ANITI, LAAS-DISCO, LAAS, Comue de Toulouse), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Jean-Michel Loubes (IMT, REGALIA), Raul Sena Ferreira (LAAS)

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A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

arXiv:2510. 26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience.

By Laura Jiang, Reza Ryan, Qian Li, Nasim Ferdosian

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arXiv Machine Learning

Sep 4

Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

The paper introduces a statistical feature augmentation technique that encodes behavioral interaction statistics into the input space for dynamic graph anomaly detection. Experiments on Reddit, Wikipedia, and MOOC datasets across seven models—both continuous-time and discrete-time—show that this augmentation consistently improves detection performance compared to models trained on original embeddings. The enriched input also facilitates fine-grained post-hoc analysis of behavioral importance, linking classical network analysis with deep learning.

By Philipp Schlinge, Jean-Luc Schnipper, Martin Atzmueller

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