arXiv AI
Co-Evolving Zero-Day Jamming: Adaptive Attack Synthesis and Graph Attention-Based Online Detection
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arXiv Machine Learning
Jul 22
Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networks
arXiv:2509. 17987v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have emerged as powerful models for anomaly detection in sensor networks, particularly when analyzing multivariate time series.
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.
arXiv AI
Jun 6
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning
arXiv:2503. 01734v3 Announce Type: replace-cross Abstract: Attacks on machine learning models have been extensively studied through stateless optimization.
arXiv Machine Learning
Aug 24
TH-GNN: Heterogeneous Temporal Graph Neural Networks for LLM-Agent Shilling Attack Detection
TH-GNN is a heterogeneous temporal graph neural network designed to detect shilling attacks generated by large language model (LLM) agents. It combines a two‑layer Heterogeneous Graph Transformer with per‑type and per‑relation attention, learnable sinusoidal temporal encodings, cross‑modal attention that fuses user embeddings with frozen RoBERTa representations of reviews and item descriptions, and a GRU that models log inter‑arrival times. Across five attack families and four benchmark datasets, TH‑GNN achieves a grand‑mean F1 score of 0.870, surpassing the best text‑only baseline on Agent4SR attacks by 10.9 percentage points and 11.5 percentage points at the lowest injection rate.
arXiv AI
Jun 17
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).
arXiv AI
Sep 1
Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL
arXiv:2608.29054v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly i...