ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks

• URL: http://arxiv.org/abs/2503.16693v1
• Date: Thu, 20 Mar 2025 20:25:32 GMT
• Title: ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks
• Authors: Zhan Cheng, Bolin Shen, Tianming Sha, Yuan Gao, Shibo Li, Yushun Dong,
• Abstract summary: Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GML) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs)<n>We propose ATOM, a novel real-time MEA detection framework tailored for GNNs.<n>ATOM integrates sequential modeling and reinforcement learning to dynamically detect evolving attack patterns, while leveraging $k$core embedding to capture the structural properties, enhancing detection precision.
• Score: 18.488168353080464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GMLaaS) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs), where adversaries reconstruct surrogate models by querying the victim model. Existing defense mechanisms, such as watermarking and fingerprinting, suffer from poor real-time performance, susceptibility to evasion, or reliance on post-attack verification, making them inadequate for handling the dynamic characteristics of graph-based MEA variants. To address these limitations, we propose ATOM, a novel real-time MEA detection framework tailored for GNNs. ATOM integrates sequential modeling and reinforcement learning to dynamically detect evolving attack patterns, while leveraging $k$-core embedding to capture the structural properties, enhancing detection precision. Furthermore, we provide theoretical analysis to characterize query behaviors and optimize detection strategies. Extensive experiments on multiple real-world datasets demonstrate that ATOM outperforms existing approaches in detection performance, maintaining stable across different time steps, thereby offering a more effective defense mechanism for GMLaaS environments.

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