Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD
📄 Download Paper

Hybrid-Deep Ensemble Anomaly Intelligence for Secure and Resilient Power Grid

Chibueze Favour Aririguzo, Miracle Ugomma Anunobi, Uchechi Joyce Nneji, Confidence Chigozirim Olumba, Richard Iherorochi Nneji, Nzubechi Augustine Oriaku, Grace Amarachi Megwa, Grace Ugochi Nneji

Abstract

Modern power networks are increasingly complex due to technological advancements, market deregulation, and ageing infrastructure, leading to heightened vulnerabilities and a critical need for robust anomaly detection mechanisms to ensure grid stability and resilience. Traditional anomaly detection methods and even machine learning techniques often fall short in adapting to the dynamic and intricate nature of these systems, struggling with high false alarm rates and limited detection accuracy, especially against sophisticated cyber threats. While deep learning models have emerged as promising solutions, existing approaches may not fully capture the complex feature space inherent in power system data for effective anomaly detection. To address these challenges, this project proposes a novel hybrid deep learning model, the Autoencoder- Enhanced Random Forest, which leverages an Autoencoder for unsupervised feature extraction and reconstruction error calculation, subsequently used to augment the training data for a Random Forest classifier. Evaluated on the Power System Attack Dataset, a publicly available resource for cybersecurity research, and using accuracy, precision, recall, and F1-score as evaluation metrics, the proposed system achieved a high accuracy of 93%, precision of 92%, recall of 98%, and an F1-score of 95%. These results significantly outperform existing methods like Random Forest, AdaBoost, and JRipper, demonstrating the effectiveness of the Autoencoder- Enhanced Random Forest approach for enhancing anomaly detection in modern power systems and contributing to improved grid security and operational reliability in the face of evolving E- ISSN 2489-009X , threats.

Keywords

Machine learningDeep learningAnomaly detectionPower systemAutoen

References

Chen, J., Guo, Y., Shi, K., & Yang, M. (2022). Network intrusion detection method of power monitoring system based on data mining. In Proceedings of the 2nd International Conference on Algorithms, High-Performance Computing and Artificial Intelligence (pp. 255–259). IEEE. Guo, X., & Liao, W. (2019). Multidimensional time series anomaly detection: A GRU-based Gaussian mixture variational autoencoder approach. Semantic Scholar. https://www.semanticscholar.org/paper/d28741f2d0b068ce717d1655edd181c37737b415 IEEE. (2012). Operating in the fog: Security management under uncertainty. https://ieeexplore.ieee.org/document/6269204 IEEE. (2019). Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. https://ieeexplore.ieee.org/document/8614232 Li, X., Wang, Y., Monday, H. N., & Nneji, G. U. (2025). A novel residual learning of multi- scale feature extraction model for the classification of rice grain varieties. Computers and Electronics in Agriculture, 237, 110491. Monday, H. N., Li, J., Nneji, G. U., Ukwuoma, C. C., Cai, J., Chikwendu, I., & Oluwasanmi, A. (2022a). A wavelet convolutional capsule network with modified super-resolution generative adversarial network for fault diagnosis and classification. Complex & Intelligent Systems, 8(1), 1–15. https://doi.org/10.1007/s40747-022-00733-6 Monday, H. N., Li, J., Nneji, G. U., Hossin, M. A., Nahar, S., Jackson, J., & Chikwendu, I. A. (2022b). WMR-DepthwiseNet: A wavelet multi-resolution depthwise separable convolutional neural network for COVID-19 diagnosis. Diagnostics, 12(3), 765. https://doi.org/10.3390/diagnostics12030765 Monday, H. N., Li, J., Nneji, G. U., Hossin, M. A., Nahar, S., Jackson, J., & Ejiyi, C. J. (2022c). COVID-19 diagnosis from chest X-ray images using a robust multi-resolution analysis Siamese neural network with super-resolution convolutional neural network. Diagnostics, 12(3), 741. https://doi.org/10.3390/diagnostics12030741 Monday, H. N., Nneji, G. U., Hossin, M. A., Mark, K. D., Umana, E. S., Mgbejime, G. T., & Li, J. (2025a). Enhancing ECG classification in cardiac diagnostics using adaptive focal cross-entropy loss function. IEEE Journal of Biomedical and Health Informatics. Morison, K., Wang, L., & Kundur, P. (2004). Power system security assessment. IEEE Power & Energy Magazine, 2(5), 30–39. https://doi.org/10.1109/MPAE.2004.1338120 Nneji, G. U., Cai, J., Deng, J., Hossin, M. A., Nahar, S., & Jackson, J. (2022c). Identification of diabetic retinopathy using weighted fusion deep learning based on dual-channel fundus scans. Diagnostics, 12(2), 540. https://doi.org/10.3390/diagnostics12020540 Nneji, G. U., Cai, J., Deng, J., Monday, H. N., James, E. C., & Ukwuoma, C. C. (2022d). Multi- channel based image processing scheme for pneumonia identification. Diagnostics, 12(2), 325. https://doi.org/10.3390/diagnostics12020325 Nneji, G. U., Deng, J., Monday, H. N., Cai, J., Hossin, M. A., Nahar, S., & Jackson, J. (2022b). COVID-19 identification from low-quality computed tomography using a modified enhanced super-resolution GAN plus and Siamese capsule network. Healthcare, 10(2), 403. https://doi.org/10.3390/healthcare10020403 Nneji, G. U., Monday, H. N., Pathapati, V. S. R., Nahar, S., Mgbejime, G. T., Umana, E. S., & Hossin, M. A. (2025). FFS-IML: Fusion-based statistical feature selection for machine learning-driven interpretability of chronic kidney disease. International Journal of Machine Learning and Cybernetics, 1–34. E- ISSN 2489-009X , Nneji, G. U., Cai, J., Monday, H. N., Hossin, M. A., Nahar, S., Jackson, J., & Deng, J. (2022a). Fine-tuned Siamese network with modified enhanced super-resolution GAN plus based on low-quality chest X-ray images for COVID-19 identification. Diagnostics, 12(3). https://doi.org/10.3390/diagnostics12030717 Thill, M., Konen, W., Wang, H., & Bäck, T. (2021). Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing, 112, 107751. https://doi.org/10.1016/j.asoc.2021.107751 Wu, T., Fan, H., Zhu, H., et al. (2022). Intrusion detection system combined enhanced random forest with SMOTE algorithm. EURASIP Journal on Advances in Signal Processing, 2022, 39. https://doi.org/10.1186/s13634-022-00871-6 Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2022). Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1), 112–122. https://doi.org/10.1109/TSMC.2020.2968516 Zhang, P., Li, F., & Bhatt, N. (2010). Next-generation monitoring, analysis, and control for the future smart control center. IEEE Transactions on Smart Grid, 1(2), 186–192. https://doi.org/10.1109/TSG.2010.2053855

More Articles from INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY

5-Point Block Backward Differentiation Formulae for Solving Stiff Chemical Reaction Problems of Ordinary Differential Equations

Author: Samson Yunusa, Imande Terdon Terlumun,, Etuk Emmanuel Dan, and Micah Michael

Analytical–Numerical Solution of a Temperature-Dependent Volterra Integro-Differential Model for Avian Influenza Transmission with Saturated Incidence and Environmental Feedback Via an Enhanced Adomian Decomposition Method

Author: Ejes, Valentine, Liberty Ebiwareme

Modelling Dengue Transmission Dynamics using a Two-Time Delay Differential Equation

Author: Uwakwe, J. I., Anyanwu, E., Inalegwu, N.

Intelligent Diagnosis and Dynamic Differentiated Instruction for High-School Mathematics Concepts Based on APOS Theory - Model Development, Implementation Pathway, And Validation Plan

Author: Xi Yang, Wenzhuo Zhang

Distribution-Free Error Control for Fraud Alert Triage: Conformal Anomaly P-Values Under Class Imbalance, Concept Drift, and Calibration Contamination

Author: Olamiji Onafowokan1, Olawale Fadugba2, Deborah Okunola1, Alex Mendy1