Adaptive Deep Learning Frameworks for Predictive Cyberattack Detection across Enterprise Cloud Networks

Abstract

The rapid adoption of cloud computing, distributed applications, remote access, and interconnected enterprise systems has increased the complexity of cybersecurity monitoring. Conventional intrusion detection approaches frequently depend on static rules and predefined signatures, which can struggle to identify evolving, previously unseen, and highly distributed cyberattacks. Deep learning provides opportunities to identify complex patterns in network traffic and system behavior, while adaptive learning mechanisms can enable detection models to respond to changing attack characteristics and enterprise network conditions. This study proposes an adaptive deep learning framework for predictive cyberattack detection across enterprise cloud networks. The framework integrates cloud telemetry, network-flow information, behavioral features, deep learning, adaptive model updating, anomaly detection, and continuous performance monitoring. The proposed methodology considers heterogeneous enterprise environments containing virtual machines, containers, applications, APIs, endpoints, and cloud services. Historical and simulated cybersecurity datasets are used to train and evaluate the framework under normal and attack conditions. The research evaluates detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, and adaptability to emerging attack patterns. Concept drift detection is incorporated to identify changes in network behavior and initiate controlled model updates. The study further considers adversarial robustness, data imbalance, computational overhead, and model explainability. The proposed framework aims to provide predictive rather than exclusively reactive cyber defense by identifying behavioral indicators associated with potential attacks before significant disruption occurs