Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs

• URL: http://arxiv.org/abs/2009.00163v2
• Date: Sat, 12 Sep 2020 20:58:04 GMT
• Title: Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs
• Authors: Houxiang Fan, Binghui Wang, Pan Zhou, Ang Li, Meng Pang, Zichuan Xu, Cai Fu, Hai Li, Yiran Chen
• Abstract summary: Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications. We study the vulnerability of LPDG methods and propose the first practical black-box evasion attack.
• Score: 87.5882042724041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational studies, etc. Various LPDG methods based on graph embedding and graph neural networks have been recently proposed and achieved state-of-the-art performance. In this paper, we study the vulnerability of LPDG methods and propose the first practical black-box evasion attack. Specifically, given a trained LPDG model, our attack aims to perturb the graph structure, without knowing to model parameters, model architecture, etc., such that the LPDG model makes as many wrong predicted links as possible. We design our attack based on a stochastic policy-based RL algorithm. Moreover, we evaluate our attack on three real-world graph datasets from different application domains. Experimental results show that our attack is both effective and efficient.

Related papers

• Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification [1.4943280454145231]
  Graph Neural Networks (GNNs) have attracted substantial interest due to their exceptional performance on graph-based data. Their robustness, especially on heterogeneous graphs, remains underexplored, particularly against adversarial attacks. This paper proposes HeteroKRLAttack, a targeted evasion black-box attack method for heterogeneous graphs.
  arXiv  Detail & Related papers  (2024-08-04T08:44:00Z)
• Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks [50.19590901147213]
  Graph neural networks (GNNs) have become instrumental in diverse real-world applications, offering powerful graph learning capabilities. GNNs are vulnerable to adversarial attacks, including membership inference attacks (MIA) This paper proposes an effective two-stage defense, Graph Transductive Defense (GTD), tailored to graph transductive learning characteristics.
  arXiv  Detail & Related papers  (2024-06-12T06:36:37Z)
• Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks [4.552065156611815]
  Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. In inductive GNNs, which allow for the processing of graph-structured data without relying on predefined graph structures, are becoming increasingly important in a wide range of applications. This paper identifies a new method of performing unsupervised model-stealing attacks against inductive GNNs.
  arXiv  Detail & Related papers  (2024-05-20T18:01:15Z)
• Everything Perturbed All at Once: Enabling Differentiable Graph Attacks [61.61327182050706]
  Graph neural networks (GNNs) have been shown to be vulnerable to adversarial attacks. We propose a novel attack method called Differentiable Graph Attack (DGA) to efficiently generate effective attacks. Compared to the state-of-the-art, DGA achieves nearly equivalent attack performance with 6 times less training time and 11 times smaller GPU memory footprint.
  arXiv  Detail & Related papers  (2023-08-29T20:14:42Z)
• Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation [60.50994154879244]
  Recent studies show that Graph Neural Networks are vulnerable and easily fooled by small perturbations. In this work, we focus on the emerging but critical attack, namely, Graph Injection Attack. We propose a general defense framework CHAGNN against GIA through cooperative homophilous augmentation of graph data and model.
  arXiv  Detail & Related papers  (2022-11-15T11:44:31Z)
• Model Inversion Attacks against Graph Neural Networks [65.35955643325038]
  We study model inversion attacks against Graph Neural Networks (GNNs) In this paper, we present GraphMI to infer the private training graph data. Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.
  arXiv  Detail & Related papers  (2022-09-16T09:13:43Z)
• BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection [20.666171188140503]
  Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs. These GAD tools expose a new attacking surface, ironically due to their unique advantage of being able to exploit the relations among data. In this paper, we exploit this vulnerability by designing a new type of targeted structural poisoning attacks to a representative regression-based GAD system OddBall.
  arXiv  Detail & Related papers  (2021-06-18T08:20:23Z)
• GraphAttacker: A General Multi-Task GraphAttack Framework [4.218118583619758]
  Graph Neural Networks (GNNs) have been successfully exploited in graph analysis tasks in many real-world applications. adversarial samples generated by attackers, which achieved great attack performance with almost imperceptible perturbations. We propose GraphAttacker, a novel generic graph attack framework that can flexibly adjust the structures and the attack strategies according to the graph analysis tasks.
  arXiv  Detail & Related papers  (2021-01-18T03:06:41Z)
• 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.