Machine Learning-Driven Approaches to Combat Cybersecurity Challenges in Cloud Environments s

Abstract

In recent years, the rapid advancement of cloud computing has revolutionized the way organizations store, process, and manage data. The cloud offers numerous benefits, including scalability, cost-efficiency, and accessibility. However, alongside these advantages, significant concerns about data security have emerged. The decentralized nature of cloud environments, coupled with the vast amount of sensitive information they handle, makes them prime targets for cyberattacks and data breaches.Ensuring the security and privacy of data in cloud computing environments is paramount. Traditional security measures, while still relevant, are increasingly being challenged by sophisticated cyber threats that exploit vulnerabilities in cloud infrastructure. This necessitates the development and implementation of more robust and intelligent security solutions.Machine learning (ML) has emerged as a promising technology in the realm of cybersecurity, offering dynamic and adaptive approaches to threat detection and mitigation. By leveraging ML algorithms, security systems can analyze large datasets to identify patterns, detect anomalies, and predict potential security threats in real-time. This proactive approach is essential in an era where cyber threats are constantly evolving and becoming more complex.This paper explores the current state of data security in cloud computing, highlighting the challenges and vulnerabilities inherent in these environments. It then delves into the innovative solutions based on machine learning that are being developed to enhance data security. By examining various ML techniques and their applications in cloud security, this research aims to provide a comprehensive overview of how machine learning can be harnessed to protect data in cloud computing.The proposed method demonstrates an accuracy of 94.6%, significantly improving upon existing methods. Additionally, it achieves a mean absolute error (MAE) of 0.403 and a root mean square error (RMSE) of 0.303, indicating high precision and robustness in detecting anomalies and potential security threats. Through a detailed analysis of existing literature and case studies, this paper will demonstrate the effectiveness of machine learning-based solutions in addressing key security concerns. It will also discuss potential future directions for research and development in this critical area. Ultimately, this paper seeks to contribute to the ongoing discourse on cloud security, offering insights into how machine learning can be utilized to safeguard data and ensure the integrity of cloud computing systems.