Proactive Security in Multi-Cloud Environments: A Blockchain Integrated Real-Time Anomaly Detection and Mitigation Framework
Keywords:
Anomaly Detection, Blockchain Security, Multi-Cloud Environments, Machine Learning, Automated Mitigation, Scalability.Abstract
AegisHaven is a proactive security framework designed to safeguard Virtual Machine (VM) deployments
in dynamic multi-cloud environments. Motivated by the challenges posed by post-deployment vulnerabilities,
evolving threats, and the fragmented nature of traditional security approaches, this framework integrates blockchain
verification with real-time anomaly detection and automated mitigation mechanisms. The system leverages advanced
machine learning techniques, including Variational Autoencoders (VAEs) and Long Short-Term Memory (LSTM)
networks, to monitor runtime behavior and detect anomalies, such as zero-day attacks, with high accuracy, precision,
and recall. Upon detection, AegisHaven employs automated mitigation strategies, including isolation, rollback, and
self-healing, to ensure rapid response and recovery, minimizing downtime and operational disruptions. Blockchain
technology enhances the framework by providing immutable logs, ensuring auditability, regulatory compliance, and
tamper-proof evidence for forensic analysis. The evaluation demonstrates AegisHaven’s scalability, with the ability
to process up to 1000 entries per second, while maintaining low CPU utilization (55%) and reduced memory overhead
(1536 MB). Compared to traditional frameworks like BCALS and Logchain, AegisHaven consistently outperforms
in anomaly detection accuracy (96.5%) and mitigation success rates (98.5%), validating its robustness and efficiency.
Future enhancements will focus on optimizing machine learning models for faster detection, exploring lightweight
blockchain technologies to minimize overhead, and expanding compatibility with additional cloud providers. These
improvements position AegisHaven as a scalable, adaptable, and efficient security solution for modern multi-cloud
infrastructures.
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References
Borangiu, T., Trentesaux, D., Thomas, A., Leitão, P., & Barata, J. (2019). Digital transformation of manufacturing through cloud services and resource virtualization. Computers in Industry, 108, 150-162.
Howell, G., Franklin, J. M., Sritapan, V., Souppaya, M., & Scarfone, K. (2023). Guidelines for Managing the Security of Mobile Devices in the Enterprise (No. NIST Special Publication (SP) 800-124 Rev. 2). National Institute of Standards and Technology.
Udayakumar, P., & Anandan, R. (2024). Design and Deploy Microsoft Defender for IoT: Leveraging Cloud-based Analytics and Machine Learning Capabilities. Springer Nature.
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