Event-Driven Cloud Integration Security through Machine Learning-Based Data Synchronization and Anomaly Detection

Abstract

Event-driven cloud integration enables distributed applications to exchange data dynamically through events, message brokers, application programming interfaces, and cloud-native services. However, the increasing complexity of interconnected systems introduces security challenges, including unauthorized data access, synchronization inconsistencies, malicious event injection, and anomalous communication patterns. This study explores a machine learning-based framework for strengthening event-driven cloud integration security through intelligent data synchronization and anomaly detection. The proposed approach combines event stream processing, secure data exchange, behavioral analysis, and machine learning algorithms to identify deviations from normal integration activities. Data synchronization mechanisms are designed to maintain consistency, integrity, and availability across distributed cloud environments, while anomaly detection techniques analyze event frequency, access behavior, transaction characteristics, and communication metadata. The methodology incorporates data preprocessing, feature extraction, model training, performance evaluation, and simulated security testing to assess the effectiveness of the framework. Evaluation criteria include detection accuracy, precision, recall, F1-score, synchronization latency, and false-positive rate. The framework also considers encryption, identity verification, access control, and audit logging to support secure information exchange. By integrating predictive analytics with event-driven architectures, the proposed approach aims to improve threat visibility, reduce response time, and strengthen operational resilience. The study provides a foundation for developing adaptive, scalable, and intelligent security mechanisms for modern cloud integration environments