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1. Solar energy management as an Internet of Things (IoT) application

   https://doi.org/10.1109/IISA.2017.8316460  

   Spanias, Andreas S. (August 2017, 2017 IEEE 8th International Conference on Information, Intelligence, Systems & Applications (IISA))

   Photovoltaic (PV) array analytics and control have become necessary for remote solar farms and for intelligent fault detection and power optimization. The management of a PV array requires auxiliary electronics that are attached to each solar panel. A collaborative industry-university-government project was established to create a smart monitoring device (SMD) and establish associated algorithms and software for fault detection and solar array management. First generation smart monitoring devices (SMDs) were built in Japan. At the same time, Arizona State University initiated research in algorithms and software to monitor and control individual solar panels. Second generation SMDs were developed later and included sensors for monitoring voltage, current, temperature, and irradiance at each individual panel. The latest SMDs include a radio and relays which allow modifying solar array connection topologies. With each panel equipped with such a sophisticated SMD, solar panels in a PV array behave essentially as nodes in an Internet of Things (IoT) type of topology. This solar energy IoT system is currently programmable and can: a) provide mobile analytics, b) enable solar farm control, c) detect and remedy faults, d) optimize power under different shading conditions, and e) reduce inverter transients. A series of federal and industry grants sponsored research on statistical signal analysis, communications, and optimization of this system. A Cyber-Physical project, whose aim is to improve solar array efficiency and robustness using new machine learning and imaging methods, was launched recently 

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2. Hybrid Cyber-attack Detection in Photovoltaic Farms

   https://doi.org/10.1109/ECCE53617.2023.10362667  

   Zhang, Jinan; Ye, Jin; Song, Wenzhan; Lian, Jianming; Zhao, Dongbo; Yang, He (October 2023, IEEE Xplore)

   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. 

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3. Recent Advances in Cyberattack Detection and Mitigation Techniques for Renewable Photovoltaic Distributed Energy CPS

   Jessica Whitaker and Danda B. Rawat (June 2023, Computing Conference)

   Cyberattacks targeted to the energy cyber-physical system (ECPS), also known as the smart grid, could interrupt the electricity supply with major ramifications. Attackers identify and exploit any vulnerable portion of the energy power grid, including the inverters with solar-powered photovoltaic (PV) panels. PV presents unique challenges as electricity consumers have also become providers of solar energy for utilities. As mandates require increased PV penetration across the world for positive environmental impacts, increased cyberattacks targeted at PV systems impact reliability and efficiency within the ECPS. The new technologies continuously being introduced to manage the ECPS and ensure bi-directional communications and energy flow between components also lead to more attack surfaces, system vulnerabilities, and heightened malicious attacks. Data integrity attacks are increasing within PV systems. In this paper, we present a survey of different methods that are proposed and explored for identifying and preventing cyberattacks targeted at PV systems. The attack detection methods include voltage control, data diodes, and voltage measurement algorithms. Furthermore, we present blockchain, cyber switching, and other attack mitigation techniques for PV systems. 

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4. Detecting Cyber Threats in Non-English Hacker Forums: An Adversarial Cross-Lingual Knowledge Transfer Approach

   Ebrahimi, M; Samtani, S; Chai, Y; Chen, H. (May 2020, IEEE Symposium on Security and Privacy (IEEE S&P), Deep Learning and Security Workshop (DLS))

   The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. Many cybersecurity professionals are closely examining the international Dark Web to proactively pinpoint potential cyber threats. Despite its potential, the Dark Web contains hundreds of thousands of non-English posts. While machine translation is the prevailing approach to process non-English text, applying MT on hacker forum text results in mistranslations. In this study, we draw upon Long-Short Term Memory (LSTM), Cross-Lingual Knowledge Transfer (CLKT), and Generative Adversarial Networks (GANs) principles to design a novel Adversarial CLKT (A-CLKT) approach. A-CLKT operates on untranslated text to retain the original semantics of the language and leverages the collective knowledge about cyber threats across languages to create a language invariant representation without any manual feature engineering or external resources. Three experiments demonstrate how A-CLKT outperforms state-of-the-art machine learning, deep learning, and CLKT algorithms in identifying cyber-threats in French and Russian forums. 

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5. A Machine Learning Approach for Combating Cyber Attacks in Self-Driving Vehicles

   Berry, Hunter; Abdel-Malek, Mai A.; Ibrahim, Ahmed S. (April 2021, Proceedings of IEEE Southeastcon)

   null (Ed.)

   Self-driving vehicles are very susceptible to cyber attacks. This paper aims to utilize a machine learning approach in combating cyber attacks on self-driving vehicles. We focus on detecting incorrect data that are injected into the data bus of vehicles. We will utilize the extreme gradient boosting approach, as a promising example of machine learning, to classify such incorrect information. We will discuss in details the research methodology, which includes acquiring the driving data, preprocessing it, artificially inserting incorrect information, and finally classifying it. Our results show that the considered algorithm achieve accuracy of up to 92% in detecting the abnormal behavior on the car data bus. 

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