Results 51 to 60 of about 24,736,965 (159)

A Review of Overcurrent Protection in Smart Grids Under Cyber‐Physical Threats With a Cyber‐Physical Evaluation Framework

Energy Science &Engineering, Volume 14, Issue 8, Page 3948-3973, August 2026.
By manipulating current and voltage measurements, an assailant can induce unwanted relay action while attempting to avoid detection. Detecting advanced cyber intrusions in power protection environments requires specialised data analysis and anomaly detection methods.
Feras Alasali, Anas AlMajali, Naser Naily, Hassen Loukil, Salah AbuGhalyon, William Holderbaum, Abdelaziz Salah Saidi   +6 more
wiley   +1 more source

Application of Deep Learning in Virtual Power Plants—A Review

Energy Internet, Volume 3, Issue 2, Page 97-110, July 2026.
ABSTRACT The advancement of deep learning (DL) has substantially improved the power grid’s ability to model and optimise diverse and highly complex tasks. Although intelligent transformation is an inevitable trend, traditional approaches increasingly reveal limitations in computational efficiency, nonlinear nonconvex optimisation, and multi‐layer ...
Hongjie Zhu, Zezhen Zhang, Yang Gao, Xiao Hu, Ying Cai, Yunqi Wang, Qin Wang, Qian Ai   +7 more
wiley   +1 more source

Moving-horizon False Data Injection Attack Design against Cyber-Physical Systems

, 2023
Systematic attack design is essential to understanding the vulnerabilities of cyber-physical systems (CPSs), to better design for resiliency. In particular, false data injection attacks (FDIAs) are well-known and have been shown to be capable of ...
Anubi, Olugbenga Moses, Zheng, Yu, Olugbenga Moses Anubi, Sridhar Babu Mudhangulla, Mudhangulla, Sridhar Babu, Yu Zheng   +5 more
core   +1 more source

HSL‐CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids

Energy Conversion and Economics, Volume 7, Issue 3, Page 207-220, June 2026.
Abstract An effective method for detecting cyberattacks is essential to the security of smart grids (SGs). In SGs, data from both cyber and physical domains can support attack detection. However, existing works insufficiently consider the heterogeneity, high dimensionality, and cross‐domain correlations of multi‐source data, affecting model ...
Qize Gao, Qiuyu Lu, June Li, Zhao Peng, Guo Shen, Ming Ni   +5 more
wiley   +1 more source

Experimental validation of the RESPONSE framework against cyberattacks on cyber‐physical process systems

AIChE Journal, Volume 72, Issue 5, May 2026.
Abstract This work experimentally validates the RESPONSE (Resilient Process cONtrol SystEm) framework as a solution for maintaining safe, continuous operation of cyber‐physical process systems under cyberattacks. RESPONSE implements a dual‐loop architecture that runs a networked online controller in parallel with a hard‐isolated offline controller ...
Luyang Liu, Pu Du, Zaman Sajid, Benjamin Wilhite, Costas Kravaris, Faisal Khan   +5 more
wiley   +1 more source

False data injection attack in smart grid: Attack model and reinforcement learning-based detection method

Frontiers in Energy Research, 2023
The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass ...
Xixiang Lin, Dou An, Feifei Cui, Feiye Zhang   +3 more
doaj   +1 more source

Hierarchical Game‐Theoretic Defense Strategy for Coordinated Attacks in Hybrid AC/DC Cyber‐Physical Power Systems

Energy Internet, Volume 3, Issue 1, Page 52-65, April 2026.
ABSTRACT This paper proposes a game‐theoretic optimisation framework for designing attack and defence strategies in hybrid AC/DC cyber‐physical power systems (CPPS). The proposed framework employs a three‐layer planning hierarchy. The upper layer introduces a novel target importance index combining complex network theory and power flow entropy.
Kaishun Xiahou, Xiaomin Lu, Yuchen Zeng, Wei Du, Jiehui Zheng, Zhaoxi Liu   +5 more
wiley   +1 more source

Securing Bipartite Nonlinear Fractional-Order Multi-Agent Systems against False Data Injection Attacks (FDIAs) Considering Hostile Environment

Fractal and Fractional
This study investigated the stability of bipartite nonlinear fractional-order multi-agent systems (FOMASs) in the presence of false data injection attacks (FDIAs) in a hostile environment. To tackle this problem we used signed graph theory, the Razumikhin methodology, and the Lyapunov function method. The main focus of our proposed work is to provide a
Hanen Louati, Saadia Rehman, Farhat Imtiaz, Nafisa A. Albasheir, A.‐B. A. Mohamed, Mohammed M. A. Almazah, Azmat Ullah Khan Niazi   +6 more
openaire   +2 more sources

A Comparative Study of Decision Tree, RNNs and CNNs for Detection of False Data Injection Attacks in Cyber‐Physical Power Systems

IET Cyber-Physical Systems: Theory &Applications, Volume 11, Issue 1, January/December 2026.
Intrusion detection for FDIAs is proposed using three approaches. (1) a stack‐based model containing an expansion decision tree and neural network, (2) recurrent neural networks (RNNs) and (3) convolutional neural networks (CNNs). The first two methods are based on the initial preprocessing of row instances, whereas the last one involves the time ...
Zeeshan Haider, Dilshad Sabir, Laiq Khan, Zahid Ullah   +3 more
wiley   +1 more source

Detection of Multi‐Target False Data Injection Attacks in Networked Microgrids Considering Renewable Energy Uncertainties

IET Cyber-Physical Systems: Theory &Applications, Volume 11, Issue 1, January/December 2026.
(1) A dynamic NMG model is developed under the cyber‐physical integration framework. (2) A novel DUIO is developed that incorporates renewable energy uncertainties and load prediction errors into the observer design. (3) A multi‐target FDIA detection framework is proposed using parallel observers for simultaneous monitoring across channels and areas ...
Kaishun Xiahou, Kun Qu, Dingjie Lin, Yang Liu, Ren Liu, Hengdao Guo   +5 more
wiley   +1 more source