Deep Learning Enabled Anomaly Detection Mechanisms for Securing Critical Enterprise Workloads in Multi-Cloud Networks

Abstract

The increasing adoption of multi-cloud architectures has enabled enterprises to distribute critical workloads across heterogeneous cloud providers, improving scalability, availability, flexibility, and resource utilization. However, this distributed architecture also introduces complex security challenges arising from diverse infrastructures, dynamic workloads, inconsistent security controls, inter-cloud communication, identity dependencies, and rapidly changing network behavior. Conventional anomaly detection mechanisms based on static rules and predefined signatures may have limited effectiveness against sophisticated, previously unseen, and rapidly evolving attacks. Deep learning provides an opportunity to address these limitations by automatically learning complex representations of network and workload behavior from large-scale security telemetry. This research proposes a deep-learning-enabled anomaly detection mechanism for securing critical enterprise workloads across multi-cloud networks. The proposed approach integrates network-flow records, cloud audit logs, workload telemetry, authentication events, application behavior, and inter-cloud communication data into a unified analytical pipeline. Data preprocessing and feature engineering are followed by deep learning models capable of identifying temporal, behavioral, and contextual deviations from normal activity. Autoencoders, recurrent neural networks, convolutional architectures, and hybrid models can be evaluated to determine their suitability for multi-cloud anomaly detection. The framework emphasizes detection accuracy, false-positive reduction, scalability, adaptability, explainability, and computational efficiency. Experimental evaluation uses precision, recall, F1-score, anomaly detection rate, false-positive rate, detection latency, and resource overhead. The proposed mechanism aims to strengthen continuous security monitoring and support proactive protection of critical enterprise workloads.