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Cost Effectiveness of Sending of Routine Collection of Sinus Contents in Skull-Base Patients
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Changes in Medical Student’s Knowledge and Attitudes Toward Clinical Death After Teaching the Philosophy of Death
No full textAntiferromagnetic Artificial Neurons: A Platform for Neuromorphic Computing and Physiological Modeling
No full textManual versus mechanical cardiopulmonary resuscitation complications after successful resuscitation for out-of-hospital cardiac arrest
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Correlation of cyber and physical dynamics in power grid relays for malware detection
No full textExploits and Malicious operations modules make up the malware application that targets the electrical power system. The exploits resemble those found in conventional malware for general-purpose computing. These malicious programs infiltrate an industrial computer, i.e. relay and then release functional components. In order to take over computational functions of the relay, malware run physics-aware modules that target physical equipment. An example is fabrication of fictitious status power data that indicates a power transformer is functioning normally, but in reality, the attacks are causing a malfunction in the transformers HARMONIC PROTECTION algorithm. This research explores the relationship between harmonic activities in power transformers and their impact on system behavior in substation computers. We privilege mimic a set of harmonic malwares and a power transformer. In this study, we contribute in multiple ways: 1) we use these emulations to examine a power transformers cyberattack surface; 2) using these insights, we develop a number of attacks that harmonic malware could employ against a power transformer. 3) we use Python to simulate these cyberattacks in order to monitor and measure their harmful effects on a power transformer empirically. Also, we used HYPERSIM Simulation provided by OPAR-R, to compare it with our python simulation 4) we use the ProcMon tool to dynamically observe system activities; and v) we use machine learning models to forecast and assess the impact of cyberattacks on computer systems.The results demonstrated that the machine learning model achieved high accuracy in detecting malware-induced anomalies, with the Python simulation yielding a 91 accuracy and the HYPERSIM simulation achieving 86 accuracy in predicting cyber-physical disturbances for the type of activity “Results”. Confusion matrix analyses revealed a strong correlation between harmonic distortions and malware activity, validating the effectiveness of frequency-domain analysis in anomaly detection. Furthermore, the model successfully differentiated between normal harmonic fluctuations and malicious injections, reducing false positives while maintaining high detection precision. This study highlights the critical role of machine learning in enhancing cybersecurity resilience in power grids. By integrating real-time anomaly detection and dynamic system activity monitoring, the proposed framework offers a robust defense mechanism against evolving cyber threats targeting power transformer operations. The findings provide a foundation for future research in developing AI-driven cybersecurity solutions tailored to industrial control systems
Minutes of the Meeting of the University Senate, March 20, 2025
No full textA. INFORMATIONAL ITEMS: Senate Ad-Hoc Policy 482 Language Review Committee; Provost's Updates | B. ROLL CALL | C. APPROVAL OF THE MINUTES of February 20, 2025 | D. OLD BUSINESS: Substantive motion from the College of Arts and Sciences (CAS) to revise the College of Arts and Sciences Constitution (approved); Substantive motion from the Senate Teaching and Learning Committee to revise its charge and membership (approved) | E. NEW BUSINESS: Support for maintaining current Oakland University initiatives for students, faculty, and staff; Establishment of Master of Science (M.S.) in Smart Manufacturing | F. GOOD AND WELFARE | G. ADJOURNMEN
