International Journal of Scientific Research and Engineering Development

This growing level of digitalization and connectedness of electrical systems has resulted in greater operational efficiencies and, at the same time, has posed significant cybersecurity risks. The conventional security solutions cannot identify and act on advanced cyber threats to electrical systems. The given paper provides a detailed view of cybersecurity management within electrical systems based on machine learning-based intrusion detection systems (ML-IDS). We discuss various ML models, such as supervised, unsupervised, and deep learning models, to detect anomalous behavior of SCADA and smart grid networks. We show with simulated and real-world datasets that the use of ML-IDS can scale better than traditional rule-based detection. Also addressed in the study are the integration issues, model interpretability, and future of self-adaptive cyber defense mechanisms. The research will help utility providers and system operators build intelligent, resilient, and proactive cybersecurity positions.