November 30, 2025Scientific ReportsOpen Access

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

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Authors

DKD. KanthasamySRM Institute of Science and Technology CKC.N.S.Vinoth Kumar DKD. KanthasamySRM Institute of Science and Technology

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Implication

Integration of feature extraction and anomaly detection improves defense against cyber threats in virtual machines, indicating enhanced security measures.

Key Points

VMShield achieves 99.8% accuracy and reduces false positives by 35% compared to existing methods.
The system incorporates feature extraction and anomaly detection through a novel classification approach.
Experiments on benchmark datasets confirm scalability with detection latency around 240 ms and overhead under 7%.
Improved defense mechanisms suggest a practical solution for protecting cloud-hosted virtual machines against emerging cyber threats.

Cite This Study

Kanthasamy et al. (2025) studied this question.

synapsesocial.com/papers/692b94581d383f2b2a379010 https://doi.org/10.1038/s41598-025-26221-8

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Implication

Integration of feature extraction and anomaly detection improves defense against cyber threats in virtual machines, indicating enhanced security measures.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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