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

Area of Science:

• Cloud Computing Security
• Cybersecurity
• Machine Learning Applications

Background:

• Virtual Machines (VMs) offer flexibility but are vulnerable to stealthy cyber threats.
• Traditional defenses struggle with adaptive attacks, leading to high false positives.
• Emerging threats like resource exhaustion and lateral movement challenge current security measures.

Purpose of the Study:

• To develop a flexible and adaptive defense system for cloud-hosted Virtual Machines (VMs).
• To mitigate limitations of traditional signature- and heuristic-based security approaches.
• To provide timely threat identification and prompt mitigation with low overhead.

Main Methods:

• Implemented a pipeline combining feature extraction, anomaly detection, classification, and mitigation.
• Utilized an Adaptive Feature Encoder for behavior representation.
• Employed Density-Aware Clustering for anomaly detection and a Transformer-Boosting Classifier for threat identification.
• Integrated a Dynamic Mitigation Controller for runtime decision-making.

Main Results:

• Achieved 99.8% accuracy, 99.7% precision, and 99.6% F1-score on benchmark datasets (ToN-IoT, CSE-CIC-IDS2018).
• Reduced false positives by 35% compared to state-of-the-art methods.
• Demonstrated scalability with detection latency around 240 ms and overhead under 7%.

Conclusions:

• The proposed VMShield framework provides an accurate and scalable defense against emerging adversarial threats in cloud VMs.
• The system integrates accuracy with operational resilience for practical cloud security.
• This adaptive and scalable protection framework addresses the limitations of traditional defenses in dynamic VM environments.