Citation

Mohammed, Saad Hammood and Jit Singh, Mandeep S. and Al-Jumaily, Abdulmajeed and Tariqul Islam, Mohammad and Islam, Md. Shabiul and Alenezi, Abdulmajeed M. and Alawad, Mohamad A. and Alsoufi, Muaadh A. (2026) IG-APSO-DNN: Deep learning intrusion detection model to detect false data injection attacks in smart grids. Ad Hoc Networks, 180. p. 104053. ISSN 1570-8705

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Abstract

False Data Injection Attacks (FDIAs) present a significant threat to smart grids by manipulating measurement data, which may lead control centers to make incorrect operational decisions. Accurate and efficient detection of FDIAs is critical for ensuring reliable grid operation. Existing deep learning approaches often fail to capture both short-term local features and long-term dependencies in power grid data, and they typically show weak correlations with past and future time series information, reducing the trustworthiness of detection results. Similarly, conventional Intrusion Detection Systems (IDS) struggle to detect advanced FDIAs due to their reliance on predefined signatures and rule-based mechanisms. To overcome these limitations, we propose IG-APSO-DNN, a two-stage deep learning model for detecting FDIAs in smart grids. The first stage employs Information Gain (IG) and Adaptive Particle Swarm Optimization (APSO) for feature selection, reducing data dimensionality and improving model efficiency. The second stage uses a Deep Neural Network (DNN) to effectively capture both spatial and temporal patterns in smart grid measurements. The proposed model is evaluated on the Industrial Control System (ICS) Cyber Attack Power System Dataset, which simulates various FDIA scenarios. Results demonstrate that IG-APSO-DNN significantly outperforms traditional methods, improving key performance metrics including detection accuracy, precision, recall, and F-measure, while ensuring reliable operation of the smart grid. This study presents a robust anomaly-based IDS framework and highlights future directions, such as real-world validation, adaptive learning, exploration of novel optimization algorithms, and addressing scalability and real-time processing challenges.

Item Type:	Article
Uncontrolled Keywords:	Smart grid, Cybersecurity, AI, Deep Learning, FDIAs, IDS
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	10 Feb 2026 07:02
Last Modified:	10 Feb 2026 07:02
URII:	http://shdl.mmu.edu.my/id/eprint/15310

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