Leveraging AI Techniques for Enhanced Cloud and Fog Com- puting: A Comprehensive Review of Intrusion Detection, Energy Optimization, Resource Allocation, and Cybersecurity Challenges in Decentralized Environments

Abstract

Cloud and fog computing systems are increasingly becoming the backbone of modern IT infrastructure, providing scalable and efficient solutions for data processing, storage, and communication across various applications. However, these environments face significant challenges, such as security threats, resource management inefficiencies, and the need for optimized performance under varying workload conditions. The integration of artificial intelligence (AI) and machine learning (ML) techniques offers promising solutions to these issues, enhancing cloud and fog computing by providing advanced capabilities for intrusion detection, energy optimization, resource allocation, and cybersecurity. This paper presents a comprehensive review of the state-of-the-art AI-driven approaches applied to cloud and fog computing environments, highlighting key methodologies, frameworks, and technologies that address pressing concerns in decentralized systems. Specifically, we explore AI-based intrusion detection systems that mitigate distributed denial-of-service (DDoS) attacks, energy-efficient algorithms that balance cost and performance, and adaptive resource allocation frameworks that optimize infrastructure scalability. Furthermore, the study delves into the use of deep learning models for anomaly detection and fault tolerance, providing robust mechanisms to enhance the reliability and security of cloud services. By examining the latest advancements and their practical implications, this paper aims to provide a thorough understanding of how AI and ML technologies are transforming cloud and fog computing landscapes, driving innovations that can meet the ever-evolving demands of digital ecosystems. The review consolidates research findings from various studies, offering insights into the current trends and future directions in AI-driven cloud and fog computing solutions.