The Cyberattack Correlation and Prevention for Distribution Systems via Machine Learning.

Cyberattacks on critical infrastructure, particularly power distribution systems, have increased in frequency and sophistication. Traditional security approaches often fail to provide real-time threat detection and correlation, leading to delayed response and significant operational disruptions. This paper presents a machine learningbased framework for cyberattack correlation and prevention in distribution systems. The proposed system leverages anomaly detection, pattern recognition, and predictive analytics to detect and mitigate cyber threats before they escalate. By analyzing network traffic, system logs, and threat intelligence data, our approach identifies attack patterns and provides automated mitigation strategies. Experimental results demonstrate that the proposed model significantly improves detection accuracy, reduces false positives, and enhances the resilience of power distribution networks against cyber threats.