Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification

• URL: http://arxiv.org/abs/2312.07870v1
• Date: Wed, 13 Dec 2023 03:17:05 GMT
• Title: Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification
• Authors: Bang Wu, Xingliang Yuan, Shuo Wang, Qi Li, Minhui Xue, Shirui Pan,
• Abstract summary: We introduce a groundbreaking approach to protect GNN models in Machine Learning from model-centric attacks. Our approach includes a comprehensive verification schema for GNN's integrity, taking into account both transductive and inductive GNNs. We propose a query-based verification technique, fortified with innovative node fingerprint generation algorithms.
• Score: 68.86863899919358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment of Graph Neural Networks (GNNs) within Machine Learning as a Service (MLaaS) has opened up new attack surfaces and an escalation in security concerns regarding model-centric attacks. These attacks can directly manipulate the GNN model parameters during serving, causing incorrect predictions and posing substantial threats to essential GNN applications. Traditional integrity verification methods falter in this context due to the limitations imposed by MLaaS and the distinct characteristics of GNN models. In this research, we introduce a groundbreaking approach to protect GNN models in MLaaS from model-centric attacks. Our approach includes a comprehensive verification schema for GNN's integrity, taking into account both transductive and inductive GNNs, and accommodating varying pre-deployment knowledge of the models. We propose a query-based verification technique, fortified with innovative node fingerprint generation algorithms. To deal with advanced attackers who know our mechanisms in advance, we introduce randomized fingerprint nodes within our design. The experimental evaluation demonstrates that our method can detect five representative adversarial model-centric attacks, displaying 2 to 4 times greater efficiency compared to baselines.

Related papers

• Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics [13.93535590008316]
  Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks. We devised a novel detection method that creatively leverages graph-level explanations. Our results show that our method can achieve high detection performance, marking a significant advancement in safeguarding GNNs against backdoor attacks.
  arXiv  Detail & Related papers  (2024-03-26T22:41:41Z)
• Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks [8.629862888374243]
  We propose the first formalization of adversarial attacks specifically tailored for GNN in network intrusion detection. We outline and model the problem space constraints that attackers need to consider to carry out feasible structural attacks in real-world scenarios. Our findings demonstrate the increased robustness of the models against classical feature-based adversarial attacks.
  arXiv  Detail & Related papers  (2024-03-18T14:40:33Z)
• A Simple and Yet Fairly Effective Defense for Graph Neural Networks [18.140756786259615]
  Graph Neural Networks (GNNs) have emerged as the dominant approach for machine learning on graph-structured data. Existing defense methods against small adversarial perturbations suffer from high time complexity. This paper introduces NoisyGNNs, a novel defense method that incorporates noise into the underlying model's architecture.
  arXiv  Detail & Related papers  (2024-02-21T18:16:48Z)
• HGAttack: Transferable Heterogeneous Graph Adversarial Attack [63.35560741500611]
  Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce. This paper introduces HGAttack, the first dedicated gray box evasion attack method for heterogeneous graphs.
  arXiv  Detail & Related papers  (2024-01-18T12:47:13Z)
• ELEGANT: Certified Defense on the Fairness of Graph Neural Networks [94.10433608311604]
  Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks. malicious attackers could easily corrupt the fairness level of their predictions by adding perturbations to the input graph data. We propose a principled framework named ELEGANT to study a novel problem of certifiable defense on the fairness level of GNNs.
  arXiv  Detail & Related papers  (2023-11-05T20:29:40Z)
• EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks [51.42338058718487]
  Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter.
  arXiv  Detail & Related papers  (2022-05-27T10:48:14Z)
• Unveiling the potential of Graph Neural Networks for robust Intrusion Detection [2.21481607673149]
  We propose a novel Graph Neural Network (GNN) model to learn flow patterns of attacks structured as graphs. Our model is able to maintain the same level of accuracy as in previous experiments, while state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score) under adversarial attacks.
  arXiv  Detail & Related papers  (2021-07-30T16:56:39Z)
• Membership Inference Attack on Graph Neural Networks [1.6457778420360536]
  We focus on how trained GNN models could leak information about the emphmember nodes that they were trained on. We choose the simplest possible attack model that utilizes the posteriors of the trained model. The surprising and worrying fact is that the attack is successful even if the target model generalizes well.
  arXiv  Detail & Related papers  (2021-01-17T02:12:35Z)
• Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case [93.37576644429578]
  Graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice. We provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.
  arXiv  Detail & Related papers  (2020-06-25T00:45:52Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.