Authors: Miss. Pemmanaboyina Durga Devi, Miss. Savarapu Suhasini
Abstract: Cloud computing has become the foundation of modern digital services by providing scalable, flexible, and cost-effective computing resources for organizations across various domains. Despite its widespread adoption, the increasing complexity of cloud infrastructures has introduced numerous security challenges, including unauthorized access, insecure configurations, application vulnerabilities, distributed denial-of-service (DDoS) attacks, and abnormal network activities. Conventional cloud security mechanisms primarily rely on rule-based detection techniques, which often struggle to identify sophisticated and previously unknown cyber threats in dynamic cloud environments. To overcome these limitations, this paper proposes an intelligent machine learning-based framework for cloud vulnerability detection and threat prevention. The proposed framework analyzes security-related information collected from system logs, network traffic records, cloud service activities, and vulnerability reports to identify malicious behavior and potential security risks. Comprehensive data preprocessing and feature engineering techniques are employed to improve data quality before training multiple machine learning models, including Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Isolation Forest. The effectiveness of these algorithms is evaluated using performance metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results demonstrate that the Random Forest model achieves superior detection performance by accurately identifying cloud vulnerabilities while maintaining a low false alarm rate. The proposed framework enables real-time threat monitoring, intelligent anomaly detection, and adaptive security analysis, thereby improving the resilience, reliability, and overall protection of distributed cloud infrastructures against evolving cyber threats.
