Enhanced Hybrid ML Framework for Smart Grid Attack Detection
DOI:
https://doi.org/10.70162/mijarcse/2025/v11/i1/v11i103Keywords:
Smart Grid Security, Intrusion Detection, Machine Learning, Hybrid Model, Cyberattack Detection, Real-Time Monitoring.Abstract
The increasing integration of information and communication technologies into smart grid infrastructures has significantly improved efficiency, automation, and reliability. However, this digital transformation has also introduced new vulnerabilities, making smart grids increasingly susceptible to sophisticated cyberattacks such as Denial-of-Service (DoS), false data injection, and replay attacks. This study aims to develop a robust, hybrid machine learning framework for intelligent intrusion detection in smart grids, addressing the limitations of existing single-model approaches in terms of generalization, real-time performance, and adaptability. The proposed framework combines three complementary models—Random Forest (RF) for static pattern classification, Long Short-Term Memory (LSTM) networks for temporal sequence modeling, and Autoencoders for unsupervised anomaly detection. Experiments were conducted on three benchmark datasets: NSL-KDD, CICIDS2017, and SWaT, covering both network-level and process-level cyberattacks. All models were trained under uniform preprocessing conditions, and their performances were evaluated using standard classification metrics and detection time analysis. The hybrid model achieved an accuracy of 96.42%, precision of 95.88%, recall of 95.55%, and F1-score of 95.71% on the NSL-KDD dataset, outperforming baseline models including SVM, RF, and LSTM. Average detection time was maintained between 4–5 ms/sample, validating its suitability for near real-time deployment. The results demonstrate the proposed framework’s effectiveness in enhancing cybersecurity for smart grids, offering a scalable, accurate, and computationally efficient solution for modern power systems.
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