Automated Vulnerability Detection and Mitigation in Virtualized Datacenter Environments

Abstract

Virtualization has become a foundational technology for modern datacenter infrastructures, enabling efficient resource utilization, scalability, and cost reduction through the consolidation of multiple virtual machines (VMs) on shared physical hardware. However, the widespread adoption of virtualization has also introduced complex security challenges, including hypervisor vulnerabilities, inter-VM attacks, misconfigurations, and rapid vulnerability propagation across shared resources. Traditional vulnerability management approaches, which rely heavily on periodic scanning and manual intervention, are often inadequate in virtualized datacenter environments due to their dynamic, large-scale, and multi-tenant nature. These limitations lead to delayed detection, increased attack surfaces, and prolonged exposure to known and unknown threats. This research proposes an automated framework for vulnerability detection and mitigation specifically tailored for virtualized datacenter environments. The proposed system continuously monitors virtual machines, hypervisors, and virtual networks to collect security-relevant data, which is then analyzed using automated detection mechanisms to identify potential vulnerabilities and anomalous behaviors in real time. Upon detection, an integrated mitigation engine dynamically selects and executes appropriate countermeasures such as automated patch deployment, virtual machine isolation, network access restriction, or live migration, based on predefined security policies and risk assessment metrics. The framework emphasizes minimal human intervention, reduced response time, and operational continuity, ensuring that security actions do not significantly disrupt service availability. Experimental evaluation demonstrates that the proposed approach improves vulnerability detection accuracy, reduces mitigation latency, and maintains acceptable system performance overhead when compared to conventional vulnerability management techniques. The results highlight the effectiveness of automation in enhancing the security posture of virtualized datacenters and underscore its potential for deployment in large-scale cloud and enterprise environments.