Interplanetary Cloud Computing for Space-Based AI Systems

Abstract

In the evolving landscape of space exploration, securing interplanetary cloud computing systems 
poses unprecedented challenges. This paper introduces a novel approach to addressing these 
challenges by integrating Generative AI with self-learning clouds in a decentralized governance 
framework. We propose a model that harnesses the capabilities of Generative AI to simulate and 
predict potential security threats, while self-learning clouds continuously adapt and enhance their 
threat detection and response mechanisms. The decentralized nature of this model ensures 
resilience and scalability, crucial for managing the complex and distributed nature of interplanetary 
cloud systems. Through extensive simulations, we evaluate the performance of this approach in 
terms of detection accuracy, false positive and negative rates, response time, and system uptime. 
The results indicate significant improvements over traditional centralized models, demonstrating 
enhanced accuracy, faster response times, and higher operational reliability. This paper also 
explores the implications of these findings for future space missions and terrestrial applications, 
suggesting that the principles and technologies developed could be adapted to other distributed 
systems, such as data centers and smart grids. Our study underscores the potential of Generative 
AI and self-learning clouds to revolutionize space-based cybersecurity, providing a robust 
framework for managing the security challenges of interplanetary cloud computing.