התקפה עקיפה ישירה על זיהוי תקלות ברשתות סנסורים

תקציר מקורי באנגליתarXiv:2509.17987v2 Announce Type: replace 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 d