Predictive Security Analytics for Multi-Cloud Enterprise Environments Using Deep Learning and Autonomous Threat Detection
DOI:
https://doi.org/10.15662/IJEETR.2024.0605025Keywords:
Predictive security analytics, multi-cloud security, deep learning, autonomous threat detection, cybersecurity, anomaly detection, cloud security, threat intelligence, behavioral analytics, enterprise securityAbstract
The increasing adoption of multi-cloud architectures has expanded enterprise computing capabilities while simultaneously creating complex cybersecurity challenges. Organizations operating across heterogeneous cloud platforms must manage diverse identities, network configurations, workloads, APIs, data repositories, and security controls. Conventional security monitoring approaches often rely on static rules, signature-based detection, and retrospective analysis, which may be inadequate for identifying sophisticated and previously unseen threats in dynamic cloud environments. This paper proposes a predictive security analytics framework that integrates deep learning with autonomous threat detection for multi-cloud enterprise environments. The proposed framework combines cloud audit logs, network telemetry, identity events, workload behavior, application activity, and security alerts to construct a unified security analytics layer. Deep learning models analyze temporal and behavioral patterns to identify anomalies and estimate potential security risks before they develop into significant incidents. An autonomous detection component continuously evaluates security events, correlates evidence across cloud environments, prioritizes suspicious activities, and initiates predefined response workflows under controlled authorization policies. The research methodology employs a multi-cloud experimental environment containing representative enterprise workloads and simulated security events. Model performance will be evaluated using detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, and computational overhead. Comparative evaluation against conventional machine-learning and rule-based detection approaches will determine the effectiveness of the proposed architecture. The research aims to demonstrate how predictive analytics and controlled autonomous detection can improve visibility, early threat identification, and coordinated security monitoring across heterogeneous cloud infrastructures
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