Cover Image

Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bilot, Tristan, Madhoun, Nour El, Agha, Khaldoun Al, Zouaoui, Anis
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

PIDSMaker: Building and Evaluating Provenance-based Intrusion Detection Systems
by: Bilot, Tristan, et al.
Published: (2026)

Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks
by: Martinez-Lopez, Fernando, et al.
Published: (2024)

DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
by: Sui, Hao, et al.
Published: (2024)

Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach
by: Stein, Kyle, et al.
Published: (2024)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)

Adversarial Attacks on Locally Private Graph Neural Networks
by: Varun, Matta, et al.
Published: (2026)

ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
by: Cheng, Zhan, et al.
Published: (2025)

Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networks
by: Hafez, Ahmad
Published: (2024)

Safeguarding Graph Neural Networks against Topology Inference Attacks
by: Fu, Jie, et al.
Published: (2025)

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
by: Dong, Xuewen, et al.
Published: (2025)

Link Stealing Attacks Against Inductive Graph Neural Networks
by: Wu, Yixin, et al.
Published: (2024)

Adversarial Attacks on Graph Neural Networks via Meta Learning
by: Zügner, Daniel, et al.
Published: (2019)

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
by: Song, Zeyu, et al.
Published: (2023)

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
by: Tang, Xinyu, et al.
Published: (2023)

Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
by: Liu, Xin, et al.
Published: (2024)

Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems
by: ElShehaby, Mohamed, et al.
Published: (2025)

Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
by: Li, Jiate, et al.
Published: (2025)

Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)

On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detection
by: Hannan, Md Abdul, et al.
Published: (2025)

A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks
by: Menssouri, Safaa, et al.
Published: (2025)

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
by: Gosch, Lukas, et al.
Published: (2024)

MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning
by: Li, Jiliang, et al.
Published: (2024)

TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning
by: Lv, Mingqi, et al.
Published: (2024)

Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks
by: Atitallah, Safa Ben, et al.
Published: (2024)

Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
by: Xu, Renjie, et al.
Published: (2024)

Unlearning Inversion Attacks for Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2025)

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
by: Marfo, William, et al.
Published: (2024)

Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning
by: Atitallah, Safa Ben, et al.
Published: (2024)

In-Context Unlearning: Language Models as Few Shot Unlearners
by: Pawelczyk, Martin, et al.
Published: (2023)

MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection
by: Xu, Xiongxiao, et al.
Published: (2023)

Indiscriminate Data Poisoning Attacks on Neural Networks
by: Lu, Yiwei, et al.
Published: (2022)

From Flows to Words: Can Zero-/Few-Shot LLMs Detect Network Intrusions? A Grammar-Constrained, Calibrated Evaluation on UNSW-NB15
by: Rehman, Mohammad Abdul, et al.
Published: (2025)

Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
by: Mukherjee, Kunal, et al.
Published: (2026)

Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention
by: Sha, Qiuwu, et al.
Published: (2025)

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
by: Jiang, Laura, et al.
Published: (2025)

Provable Privacy Attacks on Trained Shallow Neural Networks
by: Smorodinsky, Guy, et al.
Published: (2024)

Defending against Backdoor Attack on Deep Neural Networks
by: Cheng, Hao, et al.
Published: (2020)

Attacks on Node Attributes in Graph Neural Networks
by: Xu, Ying, et al.
Published: (2024)

Backdoor Attack on Vertical Federated Graph Neural Network Learning
by: Yang, Jirui, et al.
Published: (2024)

On Stealing Graph Neural Network Models
by: Podhajski, Marcin, et al.
Published: (2025)