arXiv AI June 17, 2026 By Sozan Sulaiman Maghdid, Tarik Ahmed Rashid, Shavan Askar

Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict

arXiv:2606. 17119v1 Announce Type: cross Abstract: Physical cyber systems have brought about new threats and challenges in detection and immediate response.

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

robotics

arXiv Machine Learning

Jun 11

DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace

arXiv:2606. 11687v1 Announce Type: cross Abstract: Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century.

By Marius Bayizere

agents robotics multimodal safety

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 7

Target-Aware Interaction-Guided Reinforcement Learning for Black-Box Node Injection Attacks on Graph Neural Networks

arXiv:2607. 04091v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved remarkable performance in graph representation learning, yet their inherent vulnerability to adversarial attacks poses severe security risks.

By Yi Lan, Ye Yuan

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 2

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

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.

By Yuchen Zhang, Ning Xi, Pengbin Feng, Shigang Liu, Jianfeng Ma, Yulong Shen, Yanan Sun, Xiaolin Zhou

llms robotics multimodal benchmarks safety

arXiv Machine Learning

Jun 9

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

benchmarks

arXiv Machine Learning

Jun 10

Boosting Graph Robustness Against Backdoor Attacks: An Over-Similarity Perspective

arXiv:2502. 01272v3 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have achieved notable success in tasks such as social and transportation networks.

By Chang Liu, Hai Huang, Yujie Xing, Xingquan Zuo

rag

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DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace

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

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

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

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