@inproceedings{zheng-etal-2024-improving-robustness,
title = "Improving Robustness of {GNN}-based Anomaly Detection by Graph Adversarial Training",
author = "Zheng, Xiangping and
Wu, Bo and
Zhang, Alex X. and
Li, Wei",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.779",
pages = "8902--8912",
abstract = "Graph neural networks (GNNs) play a fundamental role in anomaly detection, excelling at the identification of node anomalies by aggregating information from neighboring nodes. Nonetheless, they exhibit vulnerability to attacks, with even minor alterations in the graph structure or node attributes resulting in substantial performance degradation. To address this critical challenge, we introduce an innovative mechanism for graph adversarial training, meticulously designed to bolster GNN-based anomaly detection systems against potential poisoning attacks. This novel approach follows a two-step framework. (1) In the initial phase, we employ a Multiple-Objective Generative Adversarial Attack (MO-GAA), which focuses on generating feature modifications and inducing structural disruptions within the graph. Its primary objective is to mimic the adversarial behavior of potential attackers on the anomaly detection graph, with the explicit intention of confounding the anomaly detector. (2) In the subsequent stage, we introduce Purification-Based Adversarial Attack Defense (PB-AAD), a method specifically designed to rectify any contamination and restore the integrity of the graph. The central aim of PB-AAD is to counteract the destructive actions carried out by potential attackers. Our empirical findings, derived from extensive experiments conducted on four real-world anomaly detection datasets, serve to demonstrate how MO-GAA systematically disrupts the graph, compromising the effectiveness of GNN-based detectors, while PB-AAD effectively mitigates these adversarial actions, thereby enhancing the overall robustness of GNN-based anomaly detectors.",
}
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<abstract>Graph neural networks (GNNs) play a fundamental role in anomaly detection, excelling at the identification of node anomalies by aggregating information from neighboring nodes. Nonetheless, they exhibit vulnerability to attacks, with even minor alterations in the graph structure or node attributes resulting in substantial performance degradation. To address this critical challenge, we introduce an innovative mechanism for graph adversarial training, meticulously designed to bolster GNN-based anomaly detection systems against potential poisoning attacks. This novel approach follows a two-step framework. (1) In the initial phase, we employ a Multiple-Objective Generative Adversarial Attack (MO-GAA), which focuses on generating feature modifications and inducing structural disruptions within the graph. Its primary objective is to mimic the adversarial behavior of potential attackers on the anomaly detection graph, with the explicit intention of confounding the anomaly detector. (2) In the subsequent stage, we introduce Purification-Based Adversarial Attack Defense (PB-AAD), a method specifically designed to rectify any contamination and restore the integrity of the graph. The central aim of PB-AAD is to counteract the destructive actions carried out by potential attackers. Our empirical findings, derived from extensive experiments conducted on four real-world anomaly detection datasets, serve to demonstrate how MO-GAA systematically disrupts the graph, compromising the effectiveness of GNN-based detectors, while PB-AAD effectively mitigates these adversarial actions, thereby enhancing the overall robustness of GNN-based anomaly detectors.</abstract>
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%0 Conference Proceedings
%T Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training
%A Zheng, Xiangping
%A Wu, Bo
%A Zhang, Alex X.
%A Li, Wei
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zheng-etal-2024-improving-robustness
%X Graph neural networks (GNNs) play a fundamental role in anomaly detection, excelling at the identification of node anomalies by aggregating information from neighboring nodes. Nonetheless, they exhibit vulnerability to attacks, with even minor alterations in the graph structure or node attributes resulting in substantial performance degradation. To address this critical challenge, we introduce an innovative mechanism for graph adversarial training, meticulously designed to bolster GNN-based anomaly detection systems against potential poisoning attacks. This novel approach follows a two-step framework. (1) In the initial phase, we employ a Multiple-Objective Generative Adversarial Attack (MO-GAA), which focuses on generating feature modifications and inducing structural disruptions within the graph. Its primary objective is to mimic the adversarial behavior of potential attackers on the anomaly detection graph, with the explicit intention of confounding the anomaly detector. (2) In the subsequent stage, we introduce Purification-Based Adversarial Attack Defense (PB-AAD), a method specifically designed to rectify any contamination and restore the integrity of the graph. The central aim of PB-AAD is to counteract the destructive actions carried out by potential attackers. Our empirical findings, derived from extensive experiments conducted on four real-world anomaly detection datasets, serve to demonstrate how MO-GAA systematically disrupts the graph, compromising the effectiveness of GNN-based detectors, while PB-AAD effectively mitigates these adversarial actions, thereby enhancing the overall robustness of GNN-based anomaly detectors.
%U https://aclanthology.org/2024.lrec-main.779
%P 8902-8912
Markdown (Informal)
[Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training](https://aclanthology.org/2024.lrec-main.779) (Zheng et al., LREC-COLING 2024)
ACL