arXiv AI June 17, 2026 By Jianli Dai, Guangwei Wu, Jiacheng Li, Weiping Wang, An He, Xinjun Xiao

Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection

arXiv:2606. 17109v1 Announce Type: cross Abstract: Given their effectiveness in modeling the relational structure among network traffic flows, graph neural networks (GNNs) have been widely adopted in network intrusion detection systems (NIDSs).

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 AI.

benchmarks

arXiv Machine Learning

Jul 31

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer

rag diffusion

arXiv Machine Learning

Sep 1

Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

arXiv:2503.04404v3 Announce Type: replace Abstract: This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Altho...

By Majed Luay, Siamak Layeghy, Seyedehfaezeh Hosseininoorbin, Mohanad Sarhan, Nour Moustafa, Marius Portmann

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 AI

Jul 13

AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders

arXiv:2511. 17113v3 Announce Type: replace-cross Abstract: Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions.

By Georgios Anyfantis, Pere Barlet-Ros

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

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ARES: Anomaly Recognition Model For Edge Streams

Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

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

AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

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