Intelligent Optimization–Based Smart Grid Cyber Threat Detection Using Deep Learning and Nature-Inspired Computing Techniques Survey Paper
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Smart grids improve electricity management and ensure efficient power distribution, but they are highly vulnerable to cyber attacks such as false data injection, denial-of-service, and replay attacks. These cyber threats can disrupt power supply and compromise critical infrastructure. To address this issue, this project proposes an intelligent cyber threat detection system using classification algorithms such as K-Nearest Neighbors (KNN), Decision Tree, Support Vector Machine (SVM), and Random Forest. To further enhance performance, optimization techniques including Genetic Algorithm, Grid Search, and Particle Swarm Optimization (PSO) are applied for feature selection and hyperparameter tuning. The system is trained and tested on benchmark smart grid datasets to ensure realistic evaluation. Experimental results show that optimized models significantly improve detection accuracy and reduce false alarms, providing a reliable and efficient solution for securing smart grid environments.
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References
Y. Wang et al., “Cyber security in smart grids: A survey,” IEEE Communications Surveys & Tutorials.
A. G. Expósito et al., “Smart grid security: Threats and solutions,” IEEE Power & Energy Magazine.
S. Tan et al., “Deep learning for anomaly detection in smart grids,” IEEE Transactions on Smart Grid.
M. N. Kurt et al., “False data injection attack detection in smart grids,” IEEE Transactions on Information Forensics and Security.
X. Li et al., “PSO-based feature optimization for cyber attack detection,” Applied Soft Computing.
S. Mirjalili et al., “Grey wolf optimizer,” Advances in Engineering Software.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” IEEE International Conference on Neural Networks.
S. Mirjalili and A. Lewis, “Whale optimization algorithm,” Advances in Engineering Software.
D. Karaboga, “Artificial bee colony algorithm,” Journal of Global Optimization.
H. He and J. Yan, “Cyber-physical attacks and defenses in smart grids,” IEEE Systems Journal.
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