industrial control teams

Abstract: One-way network devices, known as data diodes, are used to defend against sophisticated cyberattacks. Partly due to their high cost, data diodes are mostly deployed in nuclear power plants and within the government for handling classified information. Although commercially available data diodes are expensive, a data diode's hardware can assembled from commodity fiber-optic network equipment. However, specialized software is needed to send data through a data diode reliably: the receiving program cannot request retransmission of dropped packets, so packet loss must be minimized and mitigated. First, we developed a minimal program to measure packet loss. We found that most packet loss was caused by the receiving program processing incoming packets too slowly, and that packet loss often occurs in clusters. Also, we discovered ways to minimize packet loss on Linux and macOS without using superuser privileges. Next, we tested three existing open source programs for one-way data transfers: netcat, UDPcast, and lidi. Although these programs were unreliable in their default configurations, we identified reliable configurations for UDPcast and lidi. Finally, we incorporated our findings into pydiode, our cross-platform program for reliable one-way data transfers.

Abstract: Multivariate time series anomaly detection (MTAD) is crucial for ensuring the safe and reliable operation of complex systems. Many existing methods learn normal patterns by training reconstruction or forecasting models on predominantly normal data. However, a large portion of these approaches rely on deterministic models and their associated point-wise output errors for anomaly scoring. Since real-world multivariate time series are inherently stochastic due to measurement noise and intrinsic system randomness, purely error-based scores can be unreliable, as large errors may arise from benign fluctuations rather than true anomalies. Probabilistic approaches address this limitation by quantifying uncertainty in model outputs. In particular, probabilistic state-space models (PSSMs) provide a principled framework by modeling stochastic system dynamics through latent state transitions and measurement noise via emission models. Despite this advantage, existing PSSM-based MTAD methods often struggle to capture long-range temporal dependencies and inter-variable dependencies, as they typically rely on noise-sensitive recurrent architectures and lack explicit cross-variable structure modeling. To address these limitations, we propose Graph-Transformer-Enhanced Probabilistic State-Space Model (GT-PSSM), a novel PSSM-based MTAD method that tightly integrates PSSM-based probabilistic modeling of stochastic dynamics with Graph Transformer-based learning of temporal and inter-variable dependencies. By jointly modeling stochasticity, long-range temporal dependence, and variable interactions within a unified probabilistic framework, GT-PSSM enables more robust anomaly detection.