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Cybersecurity Issues in Smart Grids and Future Power Systems—2nd Edition

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Special Issue Information

Dear Colleagues,

The demand for a smart and intelligent power system is growing in tandem with the increased interest in renewable energy sources. This has resulted in the adoption of smart grids, which are electrical systems that leverage digital communication infrastructure. Smart grids have several advantages, including the potential to provide consumers with a continuous power supply, reduced line losses, enhanced renewable output and storage, consumer participation in electricity markets, and demand-side responsiveness. Future power systems, also known as smart grids, will rely more on renewable energy sources such as solar and wind, as well as storage. Power electronic converters are used in renewable energy generation and storage. Each converter/inverter manufacturer has its own algorithm for programming and optimising its hardware.

Furthermore, to respond to any signal from the system operator, these converters rely on communication protocols. As a result, cyber-attacks on these smart converters/inverters are a concern. Despite the fact that numerous cyber–physical systems (CPS) have been presented, there is no universal CPS standard that can be employed with various types of converters.

Following the success of our Sensors Special Issue on “Cybersecurity Issues in Smart Grids and Future Power Systems, we would like to once again invite academics, researchers, and industry professionals from across the world to highlight their current work and define future directions.

Topics of interest include, but are not limited to:

Advanced converter control algorithms;
Synthetic inertia and virtual synchronous machines;
Cyber–physical systems for power systems;
Power quality issues in future power systems;
Lightweight encryption methods;
Intrusion detection systems;
Machine learning for cybersecurity;
Secure and trustworthy operations in the industrial Internet of Things (IoT);
Cybersecurity issues in the IoT.

Dr. Arshad Arshad
Guest Editor

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Keywords

advanced converter control algorithms
synthetic inertia and virtual synchronous machines
cyber–physical systems for power systems
power quality issues in future power systems
lightweight encryption methods
intrusion detection systems
machine learning for cybersecurity
secure and trustworthy operations in the industrial Internet of Things (IoT)
cybersecurity issues in the IoT

Related Special Issue

Published Papers (1 paper)

Research

19 pages, 4309 KiB

Open AccessArticle

Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks

by Ali Alshehri, Mahmoud M. Badr, Mohamed Baza and Hani Alshahrani

Sensors 2024, 24(10), 3236; https://doi.org/10.3390/s24103236 - 20 May 2024

Viewed by 381

Abstract

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers’ privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors’ parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers’ privacy. Full article

(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems—2nd Edition)

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