Adaptive and scalable protection framework for virtual machines leveraging deep learning and dynamic defense

Virtual Machines (VMs) serve as dynamic execution environments that trade-off workload isolation, performance, and elastic scalability in the cloud. However, the flexibility of VMs which allows for efficiency also makes them susceptible to stealthy and adaptive cyber threats such as resource exhaustion, privilege escalation, and lateral movement. In such environments, the traditional signature- and heuristic-based defenses often encounter difficulties, resulting in high false-positive rates and low-rank under changing attack conditions. To mitigate these limitations, we present a flexible defense system which combines feature extraction, anomaly detection, classification and mitigation in a single pipeline. The system consists of an Adaptive Feature Encoder for concise behavior representation, a Density-Aware Clustering for anomaly detection, a Transformer-Boosting Classifier for timely threat identification, and a Dynamic Mitigation Controller for prompt decision making at runtime, and with low overhead. Experiments on benchmark VM telemetry datasets (ToN-IoT and CSE-CIC-IDS2018) indicate that VMShield provides 99.8% accuracy, 99.7% precision, 99.6% F1-score, and reduces false positives by 35% compared to state-of-the-art baselines. Stress testing ensures scalability, keeping detection latency at ~ 240 ms and overhead under 7%. By integrating the accuracy with operational resilience, proposed adaptive and scalable protection framework offers a practical defense to protect the cloud-hosted VMs from the emerging adversarial threats.