A Machine Learning Approach to Intrusion Detection in Multi-Cloud Environments: Enhancing Cybersecurity

Abstract

Cybersecurity concerns have grown in tandem with the use of multi-cloud environments, which enterprises are using to improve scalability and flexibility. As a result, organizations must have strong intrusion detection systems to protect themselves. This paper introduces a method for intrusion detection that uses machine learning and is specifically built for multi-cloud architectures. Its purpose is to detect and react to suspicious behaviors that occur across different cloud platforms that are connected. In order to identify cyber dangers and illegal access in real time, the suggested model examines massive volumes of data from cloud traffic using techniques including ensemble approaches, supervised learning, and anomaly detection. When tested in a multi-cloud environment, our machine learning strategy demonstrated to be more accurate, faster to respond, and more adaptable than conventional intrusion detection methods. enhances cybersecurity in multi-cloud settings by providing a solution that can grow with changing threat landscapes and proactively protects against new cyber threats.