Machine Learning-Driven Threat Detection in Multi-Cloud Environments

The increasing adoption of multi-cloud environments presents new challenges in maintaining a consistent and robust security posture across heterogeneous platforms. Traditional threat detection systems, often reliant on static rules and signatures, struggle to address sophisticated, distributed, and rapidly evolving cyber threats. This paper investigates the application of machine learning (ML) techniques for dynamic and intelligent threat detection in multi-cloud ecosystems. The study explores a range of supervised, unsupervised, and reinforcement learning models for their efficacy in identifying anomalies, intrusions, and advanced persistent threats (APTs). The paper introduces a federated learning-based architecture that enables decentralized threat intelligence sharing while preserving data privacy across cloud providers. Through experimental evaluation using benchmark datasets such as UNSW-NB15 and CICIDS2017, the study demonstrate that ML-driven approaches outperform traditional intrusion detection systems in terms of accuracy, adaptability, and false positive rates. Furthermore, the study discusses implementation challenges including data heterogeneity, model drift, and regulatory constraints. My findings highlight the transformative potential of ML in enabling proactive and resilient cybersecurity strategies within multi-cloud infrastructures. This research contributes to the development of intelligent, scalable, and privacy.