Machine Learning Approaches for Cybersecurity in Distributed Cloud Infrastructures

Dzovani Sandy Putra Prayitno; Shesilia  Wibowo; Irene Apriani  Widjaya; Aris  Martono; Zeze  Nanle

doi:10.34306/ajri.v7i2.1417

Authors

Dzovani Sandy Putra Prayitno University of Raharja https://orcid.org/0009-0005-7547-7414
Shesilia Wibowo University of Raharja https://orcid.org/0009-0004-1591-4478
Irene Apriani Widjaya University of Raharja https://orcid.org/0009-0000-1723-8144
Aris Martono University of Raharja https://orcid.org/0000-0002-6464-4927
Zeze Nanle Ilearning Incorporation https://orcid.org/0009-0002-0104-1448

DOI:

https://doi.org/10.34306/ajri.v7i2.1417

Keywords:

Machine Learning, Cybersecurity, Distributed Cloud Infrastructure, Threat Detection, Digital Transformation

Abstract

Rapid cloud adoption has transformed enterprise IT infrastructures, but also introduces complex cybersecurity challenges due to the distributed and dynamic nature of cloud environments, increasing exposure to sophisticated cyber threats. This study aims to design and evaluate machine learning-based approaches to enhance cybersecurity in distributed cloud infrastructures, focusing on improving threat detection accuracy, scalability, and operational efficiency in multi-cloud environments. The proposed method employs a layered machine learning framework integrating supervised and unsupervised algorithms to detect intrusions, anomalous behaviors, and policy violations across distributed cloud nodes, supported by real-time data collection and adaptive model training. A methodological illustration indicates that machine learning approaches can achieve higher detection accuracy approximately 90% compared to traditional rule based systems approximately 78%, while reducing false-positive rates from around 22% to 10%, and experimental results further confirm improved detection performance, reduced false positives, and faster response times while maintaining scalability under increasing workloads. These findings demon- strate that machine learning-driven cybersecurity solutions provide a more adaptive, scalable, and effective defense mechanism, supporting secure and sustainable digital transformation in modern cloud environments.

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