Autonomous Vulnerability Detection in Enterprise Cloud Infrastructure Through Deep Learning and Intelligent Security Analytics
DOI:
https://doi.org/10.15662/IJEETR.2025.0706055Keywords:
Autonomous Vulnerability Detection, Enterprise Cloud Infrastructure, Deep Learning, Intelligent Security Analytics, Anomaly Detection, Risk Prioritization, Automated CybersecurityAbstract
Enterprise cloud infrastructure supports scalable computing, distributed applications, and critical business operations, but its complexity introduces vulnerabilities across virtual machines, containers, APIs, identities, networks, and software dependencies. Conventional vulnerability assessment methods often rely on signature-based scanning, predefined rules, and periodic inspections, limiting their ability to identify complex attack patterns and emerging weaknesses. This research proposes an autonomous vulnerability detection framework integrating deep learning and intelligent security analytics to improve cloud security assessment. The framework combines infrastructure configuration analysis, vulnerability intelligence, behavioral monitoring, and automated risk prioritization to identify potentially exploitable weaknesses. Deep learning models analyze security telemetry, application logs, network events, and software component information to detect suspicious patterns and previously unrecognized anomalies. An intelligent analytics engine correlates detection outputs with vulnerability severity, asset criticality, exposure, and service dependencies to generate contextual risk assessments. Automated workflows support alert generation, remediation recommendations, continuous monitoring, and controlled verification of corrective actions. The proposed methodology evaluates detection precision, recall, false-positive rates, processing latency, scalability, and resource consumption against conventional security scanning and machine learning baselines. Explainability, model validation, and human oversight are incorporated to improve decision transparency and prevent unsafe automated remediation. The framework aims to strengthen proactive vulnerability management, reduce unnecessary investigation, and improve security visibility across heterogeneous cloud environments. Its effectiveness must be established through reproducible experiments and comparative performance evaluation under realistic enterprise workloads and simulated attack scenarios.
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