Results 71 to 80 of about 24,736,965 (159)

Detection and mitigation of false data injection attack in DC–DC synchronous boost converter: A real‐time implementation using shallow neural network model

IET Power Electronics, Volume 19, Issue 1, January/December 2026.
This article proposes a neural network‐based cyber attack detection and mitigation scheme to detect and mitigate false data injection attacks on the sensors. Neural networks are used for the prediction of duty; a combination of a data sampler and a binary attack detector detects the presence of an attack.
Venkata Siva Prasad Machina, Sreedhar Madichetty, Sriranga Suprabhath Koduru, Mohan Krishna Banda, Sukumar Mishra   +4 more
wiley   +1 more source

A Comprehensive Survey on the Usage of Machine Learning to Detect False Data Injection Attacks in Smart Grids

IEEE Open Journal of the Computer Society
This article provides a comprehensive survey on the application of machine learning techniques for detecting False Data Injection Attacks (FDIA) in smart grids.
Kiara Nand, Zhibo Zhang, Jiankun Hu
doaj   +1 more source

Review of Fault Detection Methods in Microgrids: From Conventional to Innovative Perspective

IET Renewable Power Generation, Volume 20, Issue 1, January/December 2026.
The increasing global energy demand and the limitations of conventional resources have intensified the need for flexible, reliable, and sustainable energy solutions, highlighting the importance of microgrids. This study presents a comprehensive review of fault detection and management methods in microgrids, comparing traditional, signal processing ...
Mehmet Hasir, Alper Nabi Akpolat, Kürşat Ayan   +2 more
wiley   +1 more source

LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection

Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label
Wang, Buhong, Li, Qian, Xia, Xiaofang, Zhang, Meng, Tian, Jiwei, Shen, Chao, Lin, Chenhao   +6 more
core   +1 more source

Attack Resilient Microgrid: Impact Characterisation, Online Anomaly Detection and Mitigation

IET Smart Grid, Volume 9, Issue 1, January/December 2026.
This paper proposes an attack‐resilient control framework for grid‐connected microgrids that enable real‐time detection and mitigation of stealthy cyber‐physical attacks on control and measurement signals. Using an online LSTM‐based anomaly detection system and a model‐based mitigation strategy, the framework maintains near‐optimal performance even ...
Nazmus Saqib, Souradeep Bhattacharya, Kiran Kumar Challa, Manimaran Govindarasu   +3 more
wiley   +1 more source

A Deep Learning-Based Classification Scheme for False Data Injection Attack Detection in Power System

, 2021
A smart grid improves power grid efficiency by using modern information and communication technologies. However, at the same time, due to the dependence on information technology and the deep integration of electrical components and computing information
Xinying Wang, Tianjiao Pu, Kang Ma, Yucheng Ding, Ran Li, Dongxia Zhang   +5 more
core   +1 more source

Resilient Energy and Flexibility Scheduling in Interconnected Local Energy Networks via AI‐Enabled Cyber‐Integrity Enhancement

IET Smart Grid, Volume 9, Issue 1, January/December 2026.
This paper presents an AI‐enabled framework for resilient energy and flexibility scheduling in interconnected local energy networks (ILENs). By introducing a Cyber‐Connectivity Index and employing an XGBoost‐based algorithm for real‐time data correction, the model enhances operational efficiency and data integrity under cyber threats.
Ali Yazhari Kermani, Amir Abdollahi, Mehdi Shafiee   +2 more
wiley   +1 more source

Laplace-Domain Hybrid Distribution Model Based FDIA Attack Sample Generation in Smart Grids

, 2023
False data injection attack (FDIA) is a deliberate modification of measurement data collected by the power grid using vulnerabilities in power grid state estimation, resulting in erroneous judgments made by the power grid control center. As a symmetrical
Tong Zu, Naiwang Guo, Yi Wu, Zheng Zhu, Fengyong Li   +4 more
core   +1 more source

Dual‐Objective Adversarial Attack on Wind Power Forecasting: Balancing Destructiveness and Stealthiness

IET Smart Grid, Volume 9, Issue 1, January/December 2026.
This paper proposes a Dual‐Objective Adversarial Attack (DOAA) strategy that integrates attack destructiveness and stealthiness to achieve the joint optimisation of both. By adjusting the coefficient of the reconstruction loss term, the preference for attack destructiveness or stealthiness can be achieved.
Chenghao Liang, Gaoqi Liang, Xingyu Zhu, Ting Wu, Jiaqi Ruan, Guolong Liu   +5 more
wiley   +1 more source

Efficient Multi-Source Self-Attention Data Fusion for FDIA Detection in Smart Grid

, 2023
As a new cyber-attack method in power cyber physical systems, false-data-injection attacks (FDIAs) mainly disturb the operating state of power systems by tampering with the measurement data of sensors, thereby avoiding bad-data detection by the power ...
Xiangjing Su, Yingjie Tian, Naiwang Guo, Yi Wu, Fengyong Li, Qiankuan Wang   +5 more
core   +1 more source