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This study outlines a novel intrusion detection system (IDS) to detect compromised sensor data anomalies in interdependent industrial processes. The IDS used a peer-to-peer communication framework which allowed multiple programmable logic controllers (PLCs) to communicate and share sensor data. Utilizing the shared sensor data, state estimators used a long short-term memory (LSTM) machine learning algorithm to identify anomalous sensor readings connected to neighboring PLCs controlling an interdependent physical process. This study evaluated the performance of the IDS on three industrial operations aligning to a midstream oil terminal. The framework successfully detected several multi-sensor compromises during mid-stream oil terminal operations. A set of performance evaluations also showed no impact on the real-time operations of the PLC and outlined the prediction latencies of the framework. 

Abstract The Atacama Cosmology Telescope Data Release 6 (ACT DR6) power spectrum is expected to provide state-of-the-art cosmological constraints, with an associated need for precise error modeling. In this paper we design, and evaluate the performance of, an analytic covariance matrix prescription for the DR6 power spectrum that sufficiently accounts for the complicated ACT map properties. We use recent advances in the literature to handle sharp features in the signal and noise power spectra, and account for the effect of map-level anisotropies on the covariance matrix. In including inhomogeneous survey depth information, the resulting covariance matrix prescription is structurally similar to that used in thePlanckCosmic Microwave Background (CMB) analysis. We quantify the performance of our prescription using comparisons to Monte Carlo simulations, finding better than 3% agreement. This represents an improvement from a simpler, pre-existing prescription, which differs from simulations by ∼ 16%. We develop a new method to correct the analytic covariance matrix using simulations, after which both prescriptions achieve better than 1% agreement. This correction method outperforms a commonly used alternative, where the analytic correlation matrix is assumed to be accurate when correcting the covariance. Beyond its use for ACT, this framework should be applicable for future high resolution CMB experiments including the Simons Observatory (SO). 

This work presents an Intrusion Prevention System (IPS) called the Embedded Process Prediction Intrusion Prevention System (EPPIPS) to detect cyber-attacks by predicting what harm the attacks could cause to the physical process in critical infrastructure. EPIPPS is a digital twin internal to a Programmable Logic Controller (PLC). EPPIPS examines incoming command packets and programs sent to the PLC. If EPPIPS predicts these packets or programs to be harmful, EPPIPS can potentially prevent or limit the harm. EPPIPS consists of a module that examines the packets that would alter settings or actuators and incorporates a model of the physical process to aid in predicting the effect of processing the command. Specifically, EPPIPS determines whether a safety violation would occur for critical variables in the physical system. Experiments were performed on virtual testbeds involving a water tank and pipeline with a variety of command-injection attacks to determine the classification accuracy of EPPIPS. Also, uploaded programs including time and logic bombs are evaluated on whether the programs were unsafe. The results show EEPIPS is effective in predicting effects of setting changes in the PLC. EPPIPS’s accuracy is 98% for the water tank and 96% for the pipeline.