Proceedings of International Conference on Applied Innovation in IT
2025/12/22, Volume 13, Issue 5, pp.569-573
Intrusion Detection in Smart Grids by Collective Learning Algorithm and Particle Swarm Optimization Algorithm
Jenan Jader Msad, Anvar Khamdamov and Alaa Majeed Shnain Abstract: In recent years, cyber-attacks targeting the physical infrastructure of modern power systems have significantly increased, posing serious risks to society and critical services. This study investigates intrusion detection in smart grids using a hybrid approach based on Particle Swarm Optimization (PSO) and the AdaBoost ensemble classifier. To enhance detection performance, data preprocessing techniques, including handling missing values and normalization, were applied. Subsequently, PSO was utilized for feature selection, reducing the original feature set from 40 to 30 optimal attributes. These selected features were then used as input to the AdaBoost classifier. The ensemble learning mechanism of AdaBoost combines multiple weak learners to improve classification reliability by focusing on misclassified instances and aggregating their outputs. This approach enhances detection capability compared to individual classifiers. Experimental results demonstrate that the proposed method achieves an accuracy of 92.9% on the test dataset, outperforming the baseline method by approximately 3%, which confirms the effectiveness of combining feature optimization with ensemble learning for smart grid intrusion detection.
Keywords: Smart Power Grid, Intrusion Detection, AdaBoost Algorithm, Particle Optimization Algorithm.
DOI: Under indexing
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