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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.04363 |
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| _version_ | 1866910635200086016 |
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| author | Patil, Pravin Kale, Geetanjali Karmarkar, Tanmay Ghatage, Ruturaj |
| author_facet | Patil, Pravin Kale, Geetanjali Karmarkar, Tanmay Ghatage, Ruturaj |
| contents | This work proposes a secure and dynamic VM allocation strategy for multi-tenant distributed systems using the Thompson sampling approach. The method proves more effective and secure compared to epsilon-greedy and upper confidence bound methods, showing lower regret levels.,Initially, VM allocation was static, but the unpredictable nature of attacks necessitated a dynamic approach. Historical VM data was analyzed to understand attack responses, with rewards granted for unsuccessful attacks and reduced for successful ones, influencing regret levels.,The paper introduces a Multi Arm Bandit-based VM allocation policy, utilizing a Weighted Average Ensemble Learning algorithm trained on known attacks and non-attacks. This ensemble approach outperforms traditional algorithms like Logistic Regression, SVM, K Nearest Neighbors, and XGBoost.,For suspicious activity detection, a Stacked Anomaly Detector algorithm is proposed, trained on known non-attacks. This method surpasses existing techniques such as Isolation Forest and PCA-based approaches.,Overall, this paper presents an advanced solution for VM allocation policies, enhancing cloud-based system security through a combination of dynamic allocation, ensemble learning, and anomaly detection techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04363 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi Armed Bandit Algorithms Based Virtual Machine Allocation Policy for Security in Multi-Tenant Distributed Systems Patil, Pravin Kale, Geetanjali Karmarkar, Tanmay Ghatage, Ruturaj Distributed, Parallel, and Cluster Computing This work proposes a secure and dynamic VM allocation strategy for multi-tenant distributed systems using the Thompson sampling approach. The method proves more effective and secure compared to epsilon-greedy and upper confidence bound methods, showing lower regret levels.,Initially, VM allocation was static, but the unpredictable nature of attacks necessitated a dynamic approach. Historical VM data was analyzed to understand attack responses, with rewards granted for unsuccessful attacks and reduced for successful ones, influencing regret levels.,The paper introduces a Multi Arm Bandit-based VM allocation policy, utilizing a Weighted Average Ensemble Learning algorithm trained on known attacks and non-attacks. This ensemble approach outperforms traditional algorithms like Logistic Regression, SVM, K Nearest Neighbors, and XGBoost.,For suspicious activity detection, a Stacked Anomaly Detector algorithm is proposed, trained on known non-attacks. This method surpasses existing techniques such as Isolation Forest and PCA-based approaches.,Overall, this paper presents an advanced solution for VM allocation policies, enhancing cloud-based system security through a combination of dynamic allocation, ensemble learning, and anomaly detection techniques. |
| title | Multi Armed Bandit Algorithms Based Virtual Machine Allocation Policy for Security in Multi-Tenant Distributed Systems |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.04363 |