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In this paper, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. 

Despite the IEEE Power Electronics Society (PELS) establishing Technical Committee 10 on Design Methodologies with a focus on the cyber-physical security of power electronics systems, a holistic design methodology for addressing security vulnerabilities remains underdeveloped. This gap largely stems from the limited integration of computer science and power/control engineering studies in this interdisciplinary field. Addressing the inadequacy of unilateral cyber or control perspectives, this paper presents a novel four-layer cyber-physical security model specifically designed for electric machine drives. Central to this model is the innovative Control Information Flow (CIF) model, residing within the control layer, which serves as a pivotal link between the cyber layer’s vulnerable resources and the physical layer’s state-space models. By mapping vulnerable resources to control variable space and tracing attack propagation, the CIF model facilitates accurate impact predictions based on tainted control laws. The effectiveness and validity of this proposed model are demonstrated through hardware experiments involving two typical cyber-attack scenarios, underscoring its potential as a comprehensive framework for multidisciplinary security strategies. 

To address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment. 

Johnston’s organ, theDrosophilaauditory organ, is anatomically very different from the mammalian organ of Corti. However, recent evidence indicates significant cellular and molecular similarities exist between vertebrate and invertebrate hearing, suggesting thatDrosophilamay be a useful platform to determine the function of the many mammalian deafness genes whose underlying biological mechanisms are poorly characterized. Our goal was a comprehensive screen of all known orthologues of mammalian deafness genes in the fruit fly to better understand conservation of hearing mechanisms between the insect and the fly and ultimately gain insight into human hereditary deafness. We used bioinformatic comparisons to screen previously reported human and mouse deafness genes and found that 156 of them have orthologues inDrosophila melanogaster. We used fluorescent imaging of T2A-GAL4 gene trap and GFP or YFP fluorescent protein trap lines for 54 of theDrosophilagenes and found 38 to be expressed in different cell types in Johnston’s organ. We phenotypically characterized the function of strong loss-of-function mutants in three genes expressed in Johnston’s organ (Cad99C,Msp-300, andKoi) using a courtship assay and electrophysiological recordings of sound-evoked potentials.Cad99CandKoiwere found to have significant courtship defects. However, when we tested these genes for electrophysiological defects in hearing response, we did not see a significant difference suggesting the courtship defects were not caused by hearing deficiencies. Furthermore, we used a UAS/RNAi approach to test the function of seven genes and found two additional genes,CG5921andMyo10a, that gave a statistically significant delay in courtship but not in sound-evoked potentials. Our results suggest that many mammalian deafness genes haveDrosophilahomologues expressed in the Johnston’s organ, but that their requirement for hearing may not necessarily be the same as in mammals.