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1. Cyber Attack and Defense for Smart Inverters in a Distribution System

   Sun, C. C.; Zhu, R.; Liu, C. C. (June 2019, CIGRE D2 Colloquium, Helsinki, Finland)

   In this paper, a signature-based Intrusion Detection System (IDS) is developed to detect cyber intrusions of a distribution system with a high level penetration of solar energy. To identify cyberattack events, an attack table is constructed based on the Temporal Failure Propagation Graph (TFPG) technique. It includes the information of potential cyberattack patterns in terms of attack types and time sequence of anomaly events. Once the detected anomaly events are matched with any of the predefined attack patterns, it is judged to be a cyberattack. Since the attack patterns are distinguishable from other system failures, it reduces the false positive rate. To study the impact of cyberattacks on solar devices and validate the performance of the proposed IDS, a realistic Cyber-Physical System (CPS) simulation environment available at Virginia Tech (VT) is used to develop an interconnection between the cyber and power system models. The CPS model demonstrates how communication system anomalies can impact the physical system. The results of two example cyberattack test cases are obtained with the IEEE 13 node test feeder system and the power system simulator, DIgSILENT PowerFactory. 

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2. 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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3. A Novel Methodology for Cybersecurity Investment Optimization in Smart Grids using Attack-Defense Trees and Game Theory

   Hyder, Burhan; Govindarasu, Manimaran (February 2022, IEEE Innovative Smart Grid Technologies (ISGT))

   Securing cyber-physical systems (CPS) like the Smart Grid against cyber attacks is making it imperative for the system defenders to plan for investing in the cybersecurity resources of cyber-physical critical infrastructure. Given the constraint of limited resources that can be invested in the cyber layer of the cyber-physical smart grid, optimal allocation of these resources has become a priority for the defenders of the grid. This paper proposes a methodology for optimizing the allocation of resources for the cybersecurity infrastructure in a smart grid using attack-defense trees and game theory. The proposed methodology uses attack-defense trees (ADTs) for analyzing the cyber-attack paths (attacker strategies) within the grid and possible defense strategies to prevent those attacks. The attack-defense strategy space (ADSS) provides a comprehensive list of interactions between the attacker and the defender of the grid. The proposed methodology uses the ADSS from the ADT analysis for a game-theoretic formulation (GTF) of attacker-defender interaction. The GTF allows us to obtain strategies for the defender in order to optimize cybersecurity resource allocation in the smart grid. The implementation of the proposed methodology is validated using a synthetic smart grid model equipped with cyber and physical components depicting the feasibility of the methodology for real-world implementation. 

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4. Design of a Laboratory Scale Solar Microgrid Cyber-Physical System for Education

   https://doi.org/10.3390/electronics10131562  

   Guo, Liping; Kors, Jason (July 2021, Electronics)

   null (Ed.)

   Renewable energy sources such as solar and wind provide an effective solution for reducing dependency on conventional power generation and increasing the reliability and quality of power systems. Presented in this paper are design and implementation of a laboratory scale solar microgrid cyber-physical system (CPS) with wireless data monitoring as a teaching tool in the engineering technology curriculum. In the system, the solar panel, battery, charge controller, and loads form the physical layer, while the sensors, communication networks, supervisory control and data acquisition systems (SCADA) and control systems form the cyber layer. The physical layer was seamlessly integrated with the cyber layer consisting of control and communication. The objective was to create a robust CPS platform and to use the system to promote interest in and knowledge of renewable energy among university students. Experimental results showed that the maximum power point tracking (MPPT) charge controller provided the loads with power from the solar panel and used additional power to charge the rechargeable battery. Through the system, students learned and mastered key concepts and knowledge of multi-disciplinary areas including data sampling and acquisition, analog to digital conversion, solar power, battery charging, control, embedded systems and software programing. It is a valuable teaching resource for students to study renewable energy in CPS. 

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5. Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning

   https://doi.org/10.3390/info12080328  

   Qi, Ruobin; Rasband, Craig; Zheng, Jun; Longoria, Raul (August 2021, Information)

   null (Ed.)

   Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adversaries to launch cyber attacks, causing severe consequences such as massive blackout and infrastructure damages. Existing machine learning-based methods for detecting cyber attacks in smart grids are mostly based on supervised learning, which need the instances of both normal and attack events for training. In addition, supervised learning requires that the training dataset includes representative instances of various types of attack events to train a good model, which is sometimes hard if not impossible. This paper presents a new method for detecting cyber attacks in smart grids using PMU data, which is based on semi-supervised anomaly detection and deep representation learning. Semi-supervised anomaly detection only employs the instances of normal events to train detection models, making it suitable for finding unknown attack events. A number of popular semi-supervised anomaly detection algorithms were investigated in our study using publicly available power system cyber attack datasets to identify the best-performing ones. The performance comparison with popular supervised algorithms demonstrates that semi-supervised algorithms are more capable of finding attack events than supervised algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by augmenting with deep representation learning. 

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