Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networks

Abstract:Graph Neural Networks (GNNs) have emerged as powerful models for anomaly detection in sensor networks, particularly when analyzing multivariate time series. In this work, we introduce BETA, a novel indirect evasion attack targeting such GNN-based detectors, where the attacker is constrained to perturb sensor readings from a limited set of nodes, excluding the target sensor, with the goal of either suppressing a true anomaly or triggering a false alarm at the target node. BETA uses a graph explanatory model combined with a centrality-based pruning strategy to identify the most influential nodes, subsequently injecting carefully crafted adversarial perturbations into their features. Extensive experiments on three real-world sensor network datasets show that BETA consistently outperforms baseline attack strategies while operating under realistic constraints, reducing the F1-score of state-of-the-art GNN-based detectors by 36.07 to 50.45\% on average.

Machine Learning (cs.LG)

arXiv:2509.17987 [cs.LG]

(or arXiv:2509.17987v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2509.17987 Focus to learn more arXiv-issued DOI via DataCite

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