Advancements in Cloud Security: Leveraging Machine Learning for Threat Detection

Abstract

Cloud security is becoming increasingly vital as organizations migrate their data and services to cloud infrastructures. This paper investigates the integration of machine learning (ML) algorithms into cloud security frameworks to improve threat detection and mitigate security risks. With the advent of complex, distributed cloud environments, traditional security mechanisms often fail to detect sophisticated attacks such as Distributed Denial of Service (DDoS), Advanced Persistent Threats (APTs), and zero-day vulnerabilities. Machine learning models, including supervised, unsupervised, and reinforcement learning, offer new methodologies to detect and respond to security anomalies. The study explores how ML techniques, such as anomaly detection, classification, and clustering, can be applied to large-scale cloud data to identify malicious behaviors in real time. By employing neural networks and decision trees, cloud-based systems can learn from historical attack patterns to improve detection accuracy. The paper presents a comparative analysis of ML models used in threat detection, including Random Forest, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), demonstrating their effectiveness in enhancing cloud security postures. Moreover, it delves into the challenges of implementing ML in cloud environments, including data privacy, the risk of adversarial attacks on ML models, and the need for real-time processing of large datasets. The paper also proposes a hybrid framework that combines ML-based threat detection with traditional security measures like firewalls and intrusion detection systems (IDS). Future directions for improving ML-driven cloud security are discussed, particularly in the context of emerging technologies like edge computing and the Internet of Things (IoT).