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

Sanju Xaviar; Omid Ardakanian

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arXiv · 2509.17987

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

Sanju Xaviar·Omid Ardakanian

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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.

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Sanju Xaviar, Omid Ardakanian. 2025-09-22. Budgeted Indirect Adversarial Attack on Graph-Based Anomaly Detection in Sensor Networks. https://arxiv.org/abs/2509.17987

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