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1. Detection and Mitigation of Data Manipulation Attacks in AC Microgrids

   https://doi.org/10.1109/TSG.2019.2958014  

   Mustafa, A; Poudel, B; Bidram, A; Modares, H. (December 2019, IEEE transactions on smart grid)

   This paper presents a resilient control framework for distributed frequency and voltage control of AC microgrids under data manipulation attacks. In order for each distributed energy resource (DER) to detect any misbehavior on its neighboring DERs, an attack detection mechanism is first presented using a Kullback-Liebler (KL) divergence-based criterion. An attack mitigation technique is then proposed that utilizes the calculated KL divergence factors to determine trust values indicating the trustworthiness of the received information. Moreover, DERs continuously generate a self-belief factor and communicate it with their neighbors to inform them of the validity level of their own outgoing information. DERs incorporate their neighbors' self-belief and their own trust values in their control protocols to slow down and mitigate attacks. It is shown that the proposed cyber-secure control effectively distinguishes data manipulation attacks from legitimate events. The performance of proposed secure frequency and voltage control techniques is verified through the simulation of microgrid tests system implemented on IEEE 34-bus test feeder with six DERs. 

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2. A Hybrid Optimization and Deep Learning Algorithm for Cyber-resilient DER Control

   https://doi.org/10.1109/ISGT51731.2023.10066345  

   Panahazari, Mohammad; Koscak, Matthew; Zhang, Jianhua; Hou, Daqing; Wang, Jing; Gao, David Wenzhong (January 2023, 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT))

   With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update. 

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3. Identification of Intraday False Data Injection Attack on DER Dispatch Signals

   https://doi.org/10.1109/SmartGridComm52983.2022.9960974  

   Kim, Jip; Bhela, Siddharth; Anderson, James; Zussman, Gil (October 2022, in Proc. IEEE SmartGridCom’22, Oct. 2022.)

   The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Con-currently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operational challenges. This paper investigates the vulnerability of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system. 

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4. A Remediation Framework Against False Data Injection Cyberattacks Targeting ULTC Transformers to Avoid Voltage Collapse in Smart Distribution Networks

   https://doi.org/10.1109/TIA.2025.3542717  

   Naderi, Ehsan; Asrari, Arash (February 2025, IEEE Transactions on Industry Applications)

   False data injection (FDI) attacks targeting under-load tap changing (ULTC) transformers pose a significant threat to smart distribution networks by exploiting vulnerabilities in the volt-var optimization (VVO) process, leading to potential undervoltage and voltage collapse. The increased integration of renewable energy and cyber-physical systems has expanded the attack surface, making traditional detection methods inadequate. For example, in 2023, attacks on utilities and decentralized components in the United States rose by 200%, with overall cyber threats increasing by 104%, highlighting growing vulnerabilities in distribution systems. To this end, this article proposes a two-stage remediation framework for decentralized FDI (DFDI) attacks targeting ULTC transformers. In the attack stage, vulnerabilities in ULTCs and voltage regulators are scrutinized, risking voltage collapse or blackouts in the distribution system. In the remediation stage, the distribution system operator focuses on non-attacked ULTCs, voltage regulators, distributed generation (DG) units, and smart homes to minimize reliance on compromised components. In this regard, a distinctive formulation of distribution network resilience and load management (DNRLM) problem is introduced to identify a resilient network topology and determine a situational power balance strategy. The proposed framework focuses on minimizing the system's reliance on the attacked ULTCs and voltage regulator components, thereby avoiding the intended voltage collapse caused by such DFDIs. The simulation results verify that the proposed method reduces the voltage collapse proximity index by over 60%, enhancing system resilience under DFDI attacks. 

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5. A Timing-Based Framework for Designing Resilient Cyber-Physical Systems under Safety Constraint

   https://doi.org/10.1145/3594638  

   Al Maruf, Abdullah; Niu, Luyao; Clark, Andrew; Mertoguno, J. Sukarno; Poovendran, Radha (July 2023, ACM Transactions on Cyber-Physical Systems)

   Cyber-physical systems (CPS) are required to satisfy safety constraints in various application domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger human lives. Resilient architectures have been proposed to ensure safety of CPS under such faults and attacks via methodologies including redundancy and restarting from safe operating conditions. The existing resilient architectures for CPS utilize different mechanisms to guarantee safety, and currently, there is no common framework to compare them. Moreover, the analysis and design undertaken for CPS employing one architecture is not readily extendable to another. In this article, we propose a timing-based framework for CPS employing various resilient architectures and develop a common methodology for safety analysis and computation of control policies and design parameters. Using the insight that the cyber subsystem operates in one out of a finite number of statuses, we first develop a hybrid system model that captures CPS adopting any of these architectures. Based on the hybrid system, we formulate the problem of joint computation of control policies and associated timing parameters for CPS to satisfy a given safety constraint and derive sufficient conditions for the solution. Utilizing the derived conditions, we provide an algorithm to compute control policies and timing parameters relevant to the employed architecture. We also note that our solution can be applied to a wide class of CPS with polynomial dynamics and also allows incorporation of new architectures. We verify our proposed framework by performing a case study on adaptive cruise control of vehicles. 

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