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1. Security Vulnerabilities of Smart Meters in Smart Grid

   https://doi.org/10.1109/IECON.2019.8926992  

   Gui, Yutian ; Siddiqui, Ali Shuja ; Tamore, Suyash Mohan ; Saqib, Fareena ( October 2019 , Security Vulnerabilities of Smart Meters in Smart Grid)

   Integration of complex and high-speed electronic components in the state of art electric power system enhances the need for improved security infrastructure and resilience against invasive and non-invasive attacks on the smart grid. A modern smart grid system integrates a variety of instruments and standards to achieve cost-effective and time-effective energy measurement and management. As the fundamental component in the smart grid, the smart meter supports real-time monitoring, automatic control, and high-speed communication along with power consumption recording. However, the wide use of smart meters also increases privacy and security concerns. In this paper, we demonstrate the vulnerability of side-channel attacks on secure communication in smart grids for software-based and hardware-based implementations. 

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2. iCAAP: information-Centric network Architecture for Application-specific Prioritization in Smart Grid

   James, Anju ; Torres, George ; Shrestha, Sharad ; Tourani, Reza ; Misra, Satyajayant ( January 2021 , Innovative smart grid technologies)

   The smart grid is equipped with bi-directional information flow between its devices, aiming at automation, improved stability, resilience, and robust security. However, enabling effective and reliable communication in a smart grid is a challenging task. The majority of the proposed networking architectures fall short in addressing the key aspects of smart grid communication, including device heterogeneity, protocols and standards interoperability, and particularly application quality- of-service (QoS) requirements. In this paper, we propose iCAAP, an information-centric, QoS-aware network architecture that aims to satisfy the low latency, high bandwidth, and high reliability requirements of smart grid communications. In iCAAP, we categorize smart grid traffic (emanating from diverse applications) into three priority classes to enable preferential treatment of traffic flows. Our simulation results demonstrate the higher scalability of iCAAP in satisfying the stringent requirements of high priority traffic compared to the state-of-the-art. 

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3. iCAAP: information-Centric network Architecture for Application-specific Prioritization in Smart Grid

   https://doi.org/10.1109/ISGT49243.2021.9372162  

   James, Anju K. ; Torres, George ; Shrestha, Sharad ; Tourani, Reza ; Misra, Satyajayant ( February 2021 , 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT))

   null (Ed.)

   The smart grid is equipped with bi-directional information flow between its devices, aiming at automation, improved stability, resilience, and robust security. However, enabling effective and reliable communication in a smart grid is a challenging task. The majority of the proposed networking architectures fall short in addressing the key aspects of smart grid communication, including device heterogeneity, protocols and standards interoperability, and particularly application qualityof-service (QoS) requirements. In this paper, we propose iCAAP, an information-centric, QoSaware network architecture that aims to satisfy the low latency, high bandwidth, and high reliability requirements of smart grid communications. In iCAAP, we categorize smart grid traffic (emanating from diverse applications) into three priority classes to enable preferential treatment of traffic flows. Our simulation results demonstrate the higher scalability of iCAAP in satisfying the stringent requirements of high priority traffic compared to the state-of-the-art. 

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4. A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid

   https://doi.org/10.3390/en14185894  

   Tufail, Shahid ; Parvez, Imtiaz ; Batool, Shanzeh ; Sarwat, Arif ( September 2021 , Energies)

   null (Ed.)

   The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it from potential cyberattacks. There are millions of sensors continuously sending and receiving data packets over the network, so managing such a gigantic network is the biggest challenge. Any cyberattack can damage the key elements, confidentiality, integrity, and availability of the smart grid. The overall smart grid network is comprised of customers accessing the network, communication network of the smart devices and sensors, and the people managing the network (decision makers); all three of these levels are vulnerable to cyberattacks. In this survey, we explore various threats and vulnerabilities that can affect the key elements of cybersecurity in the smart grid network and then present the security measures to avert those threats and vulnerabilities at three different levels. In addition to that, we suggest techniques to minimize the chances of cyberattack at all three levels. 

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5. Online Testbed for Evaluating Vulnerability of Deep Learning Based Power Grid Load Forecasters

   https://doi.org/10.1109/MSCPES49613.2020.9133701  

   Neema, Himanshu ; Volgyesi, Peter ; Koutsoukos, Xenofon ; Roth, Thomas ; Nguyen, Cuong ( April 2020 , 2020 8th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems)

   Modern electric grids that integrate smart grid technologies require different approaches to grid operations. There has been a shift towards increased reliance on distributed sensors to monitor bidirectional power flows and machine learning based load forecasting methods (e.g., using deep learning). These methods are fairly accurate under normal circumstances, but become highly vulnerable to stealthy adversarial attacks that could be deployed on the load forecasters. This paper provides a novel model-based Testbed for Simulation-based Evaluation of Resilience (TeSER) that enables evaluating deep learning based load forecasters against stealthy adversarial attacks. The testbed leverages three existing technologies, viz. DeepForge: for designing neural networks and machine learning pipelines, GridLAB-D: for electric grid distribution system simulation, and WebGME: for creating web-based collaborative metamodeling environments. The testbed architecture is described, and a case study to demonstrate its capabilities for evaluating load forecasters is provided. 

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