AI-Enhanced Cloud Solutions for Real-Time Cyber Attack Mitigation and Predictive Maintenance of Autonomous Infrastructure

Abstract

In the ever-evolving landscape of cybersecurity and infrastructure management, the need for 
advanced solutions to address cyber threats and maintenance challenges is paramount. This paper 
explores the integration of Artificial Intelligence (AI) in cloud-based systems to enhance real-time 
cyber attack mitigation and predictive maintenance for autonomous infrastructure. We propose a 
novel framework that leverages AI-driven anomaly detection and predictive analytics to 
proactively identify and respond to potential threats and system failures. The framework combines 
state-of-the-art machine learning models, including Autoencoders for anomaly detection and 
Reinforcement Learning for predictive maintenance, to create a robust and adaptive system. The 
performance of these models is evaluated using real-world datasets, demonstrating their 
effectiveness in improving response times, accuracy, and overall system reliability. Our results 
show that AI-enhanced cloud solutions can significantly reduce the impact of cyber attacks and 
maintenance issues, leading to more resilient and efficient infrastructure management. This paper 
provides insights into the implementation and benefits of AI technologies in cloud environments, 
highlighting their potential to transform traditional approaches to cybersecurity and infrastructure 
maintenance