Multi Armed Bandit Algorithms Based Virtual Machine Allocation Policy for Security in Multi-Tenant Distributed Systems

TL;DR

This paper introduces a secure, dynamic VM allocation policy for multi-tenant distributed systems using Thompson sampling, ensemble learning, and anomaly detection, significantly improving security and reducing attack success rates.

Contribution

It presents a novel Multi Arm Bandit-based VM allocation strategy combined with ensemble learning and anomaly detection, outperforming traditional methods in security and efficiency.

Findings

Thompson sampling outperforms epsilon-greedy and UCB in VM allocation security.

Ensemble learning approach surpasses traditional classifiers in attack detection.

Proposed anomaly detection method exceeds existing techniques in identifying suspicious activity.

Abstract

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.