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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. Defense-in-Depth Framework for Power Transmission System against Cyber-Induced Substation Outages

   https://doi.org/10.1109/TPEC56611.2023.10078481  

   Khanna, Kush; Ravikumar, Gelli; Govindarasu, Manimaran (February 2023, IEEE)

   The energy revolution is primarily driven by the adoption of advanced communication technologies that allow for the digitization of power grids. With the confluence of Information Technology (IT) and Operational Technology (OT), energy systems are entering the larger world of Cyber-Physical Systems (CPS). Cyber threats are expected to grow as the attack surface expands, posing a significant operational risk to any cyber-physical system, including the power grid. Substations are the electricity transmission systems’ most critical assets. Substation outages caused by cyber-attacks produce widespread power outages impacting thousands of consumers. To plan and prepare for such rare yet high-impact occurrences, this paper proposes an integrated defense-in-depth framework for power transmission systems to reduce the risk of cyber-induced substation failures. The inherent resilience of physical power systems assesses cyber-attacks’ impact on critical substations. The presented approach integrates the physical implications of substation failures with cyber vulnerabilities to analyze cyber-physical risks holistically. The framework is simulated and validated for an IEEE 14-bus system. 

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5. HIL Testbed-based Auto Feature Extraction and Data Generation Framework for ML/DL-based Anomaly Detection and Classification

   https://doi.org/10.1109/ISGT59692.2024.10454202  

   Mantha, Aditya Akilesh; Hussain, Arif; Ravikumar, Gelli (February 2024, IEEE)

   In smart grid operations, sufficient data generation, including events such as faults, cyberattacks, and perturbations, is one of the key requirements for accurate Machine Learning (ML) and Deep Learning (DL) based anomaly detection and classification. However, the public datasets in power systems pose data sufficiency, bias, and uncertainty challenges. Moreover, synthetic datasets may only capture some feature vectors in power systems and complex dynamics, resulting in sub-optimal performance of ML/DL models. In this paper, we proposed the Auto Feature Extraction and Data Generation (AFEDG) framework for ML and DL models in smart grid applications such as anomaly detection and classification. We developed a Python-based Application Programming Interface (API) to automate the extraction of feature vectors and data generation processes by integrating virtual sensors. The API dynamically modulates parameters and signals within the power system model, ensuring comprehensive coverage of all event scenarios and generating unbiased, balanced datasets. The proposed API is designed to seamlessly integrate with power systems, exhibiting scalability and adaptability regardless of the number of buses within the system. We implemented the proposed AFEDG framework on a Hardware-in-the-Loop (HIL) Cyber-Physical System (CPS) testbed to generate large-scale real-time datasets. In our case study, we used the Python API to generate electric fault datasets on the modified SSN-distribution bus grid model in OPAL-RT, integrating with RT-LAB and MATLAB. 

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