Comprehensive Security Analysis and Threat Mitigation in a Wind Energy Prediction System
摘要
This study presents a comprehensive cybersecurity analysis of wind energy prediction systems that integrate Internet of Things (IoT) sensors, cloud computing, and Machine Learning (ML) algorithms. Using data collected from meteorological stations and wind turbines in Cajicá, Colombia, we developed predictive models while identifying critical security vulnerabilities in the digitalized energy infrastructure. Our research examines historical cyberattacks on critical infrastructure, analyzes attack vectors specific to IoT-enabled wind energy systems, and proposes a multi-layered security framework. The system achieved 94% prediction accuracy while maintaining robust security posture against emerging cyber threats, detecting 234 attack attempts with 99.7% accuracy during a 6-month testing period. Our findings contribute to the secure digitalization of renewable energy infrastructure, addressing the growing intersection between energy sustainability and cybersecurity resilience.