Image de couverture de livre

Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Hafez, Ahmad
Format:	Preprint
Publié:	2024
Sujets:	Cryptography and Security Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Causal Graph Profiling via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems
par: Malarkkan, Arun Vignesh, et autres
Publié: (2025)

Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Review
par: Moriano, Pablo, et autres
Publié: (2024)

DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
par: Sui, Hao, et autres
Publié: (2024)

Using Graph Theory for Improving Machine Learning-based Detection of Cyber Attacks
par: Zonneveld, Giacomo, et autres
Publié: (2024)

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
par: Jiang, Laura, et autres
Publié: (2025)

Adversarial Attacks on Graph Neural Networks via Meta Learning
par: Zügner, Daniel, et autres
Publié: (2019)

Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks
par: Bilot, Tristan, et autres
Publié: (2025)

DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
par: Zhang, Qiuchen, et autres
Publié: (2022)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
par: Zhang, Jiahao, et autres
Publié: (2026)

ADVENT: Attack/Anomaly Detection in VANETs
par: Baharlouei, Hamideh, et autres
Publié: (2024)

Graph Attention Networks with Physical Constraints for Anomaly Detection
par: Homaei, Mohammadhossein, et autres
Publié: (2026)

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications
par: Haug, Markus, et autres
Publié: (2025)

CyberGFM: Graph Foundation Models for Lateral Movement Detection in Enterprise Networks
par: King, Isaiah J., et autres
Publié: (2026)

Adversarial Attacks on Locally Private Graph Neural Networks
par: Varun, Matta, et autres
Publié: (2026)

ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
par: Cheng, Zhan, et autres
Publié: (2025)

Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection
par: Zoubir, Abdeljalil, et autres
Publié: (2024)

Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
par: Liu, Xin, et autres
Publié: (2024)

Link Stealing Attacks Against Inductive Graph Neural Networks
par: Wu, Yixin, et autres
Publié: (2024)

Safeguarding Graph Neural Networks against Topology Inference Attacks
par: Fu, Jie, et autres
Publié: (2025)

Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks
par: Dong, Xuewen, et autres
Publié: (2025)

Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data
par: Haque, Md. Ehsanul, et autres
Publié: (2025)

Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention
par: Sha, Qiuwu, et autres
Publié: (2025)

Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
par: Lin, Minhua, et autres
Publié: (2024)

Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
par: Li, Jiate, et autres
Publié: (2025)

Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications
par: Raza, Ali, et autres
Publié: (2022)

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
par: Gosch, Lukas, et autres
Publié: (2024)

Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset
par: Ahmed, Waqas
Publié: (2025)

Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection
par: Pang, Yining, et autres
Publié: (2024)

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
par: Marfo, William, et autres
Publié: (2024)

Evaluating Generalization Mechanisms in Autonomous Cyber Attack Agents
par: Lukáš, Ondřej, et autres
Publié: (2026)

Unlearning Inversion Attacks for Graph Neural Networks
par: Zhang, Jiahao, et autres
Publié: (2025)

Anomaly Detection in Graph Structured Data: A Survey
par: Lamichhane, Prabin B, et autres
Publié: (2024)

Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
par: Mukherjee, Kunal, et autres
Publié: (2026)

Machine Learning for Cyber-Attack Identification from Traffic Flows
par: Zhou, Yujing, et autres
Publié: (2025)

Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems
par: Jean, Abile, et autres
Publié: (2026)

Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
par: Xu, Renjie, et autres
Publié: (2024)

DivQAT: Enhancing Robustness of Quantized Convolutional Neural Networks against Model Extraction Attacks
par: Khaled, Kacem, et autres
Publié: (2025)

Indiscriminate Data Poisoning Attacks on Neural Networks
par: Lu, Yiwei, et autres
Publié: (2022)

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
par: Jin, Zhibo, et autres
Publié: (2024)

A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks
par: Menssouri, Safaa, et autres
Publié: (2025)