Sanju Xaviar | Searcharxiv

arXiv2026

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

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.

cs.LG↗

arXiv2023

Robust Multimodal Fusion for Human Activity Recognition

The proliferation of IoT and mobile devices equipped with heterogeneous sensors has enabled new applications that rely on the fusion of time-series data generated by multiple sensors with different modalities. While there are promising deep neural network architectures for multimodal fusion, their performance falls apart quickly in the presence of consecutive missing data and noise across multiple modalities/sensors, the issues that are prevalent in real-world settings. We propose Centaur, a multimodal fusion model for human activity recognition (HAR) that is robust to these data quality issues. Centaur combines a data cleaning module, which is a denoising autoencoder with convolutional layers, and a multimodal fusion module, which is a deep convolutional neural network with the self-attention mechanism to capture cross-sensor correlation. We train Centaur using a stochastic data corruption scheme and evaluate it on three datasets that contain data generated by multiple inertial measurement units. Centaur's data cleaning module outperforms 2 state-of-the-art autoencoder-based models and its multimodal fusion module outperforms 4 strong baselines. Compared to 2 related robust fusion architectures, Centaur is more robust, achieving 11.59-17.52% higher accuracy in HAR, especially in the presence of consecutive missing data in multiple sensor channels.

cs.LG↗