AI-Enabled Security Patch Orchestration in Cloud Infrastructure: A Comprehensive Review and Framework for Mitigating Co-Resident DDoS Attacks
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901100050776064 |
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| author | Dr.Rethishkumar S Dr.Anjana S. Chandran |
| author_facet | Dr.Rethishkumar S Dr.Anjana S. Chandran |
| contents | <p>This paper presents a comprehensive AI-enabled dynamic security patch orchestration framework designed to mitigate co-resident Distributed Denial of Service (DDoS) attacks in cloud infrastructures. Co-resident attacks exploit shared physical resources such as CPU cache, memory bandwidth, and network I/O between virtual machines (VMs). The proposed framework integrates hybrid Random Forest–Long Short-Term Memory (RF-LSTM) anomaly detection, real-time behavioral monitoring, automated micro-patch deployment, and adaptive feedback learning mechanisms. Extensive simulation using CloudSim with 100–500 VM scenarios demonstrates superior detection accuracy (97.8%), improved precision and recall, and significant reduction in mitigation latency (62% improvement) compared to static patching and signature-based IDS approaches. The framework provides scalable, proactive, and intelligent cloud defense suitable for modern multi-tenant environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18697742 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Enabled Security Patch Orchestration in Cloud Infrastructure: A Comprehensive Review and Framework for Mitigating Co-Resident DDoS Attacks Dr.Rethishkumar S Dr.Anjana S. Chandran <p>This paper presents a comprehensive AI-enabled dynamic security patch orchestration framework designed to mitigate co-resident Distributed Denial of Service (DDoS) attacks in cloud infrastructures. Co-resident attacks exploit shared physical resources such as CPU cache, memory bandwidth, and network I/O between virtual machines (VMs). The proposed framework integrates hybrid Random Forest–Long Short-Term Memory (RF-LSTM) anomaly detection, real-time behavioral monitoring, automated micro-patch deployment, and adaptive feedback learning mechanisms. Extensive simulation using CloudSim with 100–500 VM scenarios demonstrates superior detection accuracy (97.8%), improved precision and recall, and significant reduction in mitigation latency (62% improvement) compared to static patching and signature-based IDS approaches. The framework provides scalable, proactive, and intelligent cloud defense suitable for modern multi-tenant environments.</p> |
| title | AI-Enabled Security Patch Orchestration in Cloud Infrastructure: A Comprehensive Review and Framework for Mitigating Co-Resident DDoS Attacks |
| url | https://doi.org/10.5281/zenodo.18697742 |