Anomaly detection for early ransomware and spyware warning in nuclear power plant systems based on FusionGuard

Abstract

Securing critical infrastructure, particularly nuclear power plants, against emerging cyber threats necessitates innovative cybersecurity approaches. This research introduces FusionGuard, a hybrid machine learning-based anomaly detection system designed for early warnings of ransomware and spyware intrusions within nuclear power plant systems. Meticulously tailored to the unique characteristics of nuclear power plant networks, FusionGuard leverages diverse datasets encompassing normal operational behavior and historical threat data. Through cutting-edge machine learning algorithms, the system dynamically adapts to the network's baseline behavior, effectively identifying deviations indicative of ransomware or spyware activities. Rigorous experimentation and validation using real-world data and simulated attack scenarios affirm FusionGuard's proficiency in detecting anomalous behavior with remarkable accuracy and minimal false positives. The research also explores the system's scalability and adaptability to evolving attack vectors, fortifying the cybersecurity posture of nuclear power plant systems in a dynamic threat landscape. In summary, FusionGuard promises to fortify the security of nuclear power plant systems against ransomware and spyware threats by capitalizing on machine learning and anomaly detection. Serving as a sentinel, the system issues timely alerts and enables proactive responses, contributing substantively to the ongoing discourse on protecting essential systems in high-stakes environments.