Machine Learning-Based Cyber Threat Detection in Cloud Computing Environments

Abstract

The widespread adoption of cloud computing has introduced flexible and scalable computing resources but has also created increasingly complex cybersecurity challenges. Traditional security mechanisms may have difficulties detecting sophisticated and previously unknown threats in dynamic cloud environments. This study proposes a machine learning-based framework for detecting cyber threats in cloud computing infrastructures. The framework analyzes system and network data to identify abnormal behavior and potential security incidents. Supervised and anomaly-based machine learning techniques can be utilized to classify suspicious activities and generate security alerts. The research considers important factors including detection accuracy, false-positive rates, computational overhead, and adaptability to changing attack patterns. The proposed approach demonstrates how intelligent analytics can strengthen cloud security by enabling continuous monitoring and early threat identification. The findings provide a basis for developing adaptive cybersecurity mechanisms for modern cloud infrastructures.