Advanced Behavioral Analytics for User and Entity Behavior Anomaly Detection in Hybrid Cloud Environments
Keywords:
hybrid cloud security, behavioral analytics, anomaly detectionAbstract
The increasing adoption of hybrid cloud environments in enterprises has necessitated advanced mechanisms to ensure robust security and operational integrity. This research delves into the application of advanced behavioral analytics for detecting user and entity behavior anomalies in hybrid cloud environments, focusing on artificial intelligence (AI) and machine learning (ML) models to address the inherent complexity and dynamic nature of these infrastructures. Hybrid cloud environments, characterized by their interconnected public and private cloud systems, create unique challenges for security monitoring due to diverse user activities, heterogeneous workloads, and evolving threat landscapes. Establishing baseline behavior profiles for users and entities is a critical first step in addressing these challenges. This study explores supervised and unsupervised ML approaches, including clustering algorithms, such as k-means and DBSCAN, and outlier detection techniques, such as Isolation Forests and Local Outlier Factor (LOF), for modeling normal behavior patterns.
The paper also examines the challenges associated with constructing reliable baselines in hybrid cloud settings, such as the variability of workloads, the diversity of user roles, and the continuous adaptation of cloud environments. Additionally, the integration of these models with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms is evaluated. Such integration enables automated incident detection and response, reducing the time to identify and mitigate threats. Key considerations include the harmonization of data ingestion from multi-cloud sources, real-time anomaly detection capabilities, and the orchestration of automated workflows for incident handling. By leveraging anomaly detection mechanisms, this research demonstrates how hybrid cloud environments can achieve enhanced situational awareness and improved threat response.
Through case studies and experimental validations, this study provides insights into the operationalization of behavioral analytics frameworks, highlighting their effectiveness in detecting insider threats, compromised accounts, and advanced persistent threats (APTs). The results demonstrate that integrating behavioral analytics into hybrid cloud security infrastructures not only strengthens anomaly detection capabilities but also enhances the efficiency and scalability of incident management workflows. Future directions include the exploration of federated learning models to enhance privacy-preserving analytics and adaptive algorithms capable of responding to evolving threat vectors in real-time.
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