[Paper Review] Security Management Model in Cloud Computing Environment

Seyed Hossein Ahmadpanah|arXiv (Cornell University)|Nov 27, 2016

IoT and Edge/Fog Computing5 references3 citations

TL;DR

This paper proposes a dynamic, VM-centric security management model for cloud environments that integrates AHP-based VM scheduling, CUSUM for DDoS detection, and state migration for resilience. The model enhances security and efficiency through adaptive workload placement and real-time attack detection, validated via functional testing with improved threat mitigation and resource utilization.

ABSTRACT

In the cloud computing environment, cloud virtual machine (VM) will be more and more the number of virtual machine security and management faced giant Challenge. In order to address security issues cloud computing virtualization environment, this paper presents a virtual machine based on efficient and dynamic deployment VM security management model state migration and scheduling, study of which virtual machine security architecture, based on AHP (Analytic Hierarchy Process) virtual machine deployment and scheduling method, based on CUSUM (Cumulative Sum) DDoS attack detection algorithm, and the above-described method for functional testing and validation.

Motivation & Objective

• To address the growing challenge of securing a large number of virtual machines (VMs) in cloud environments.
• To develop a dynamic, state-aware VM security management model that supports efficient deployment and scheduling.
• To integrate AHP for intelligent VM deployment and scheduling decisions based on security and performance criteria.
• To implement a CUSUM-based algorithm for real-time detection of DDoS attacks.
• To validate the model’s functionality through testing, focusing on security and operational efficiency.

Proposed method

• The model uses the Analytic Hierarchy Process (AHP) to prioritize and schedule VMs based on security and performance factors.
• A CUSUM (Cumulative Sum) statistical process control algorithm is applied to detect DDoS attacks by monitoring traffic anomalies.
• VM state migration is employed dynamically to isolate or reconfigure compromised or high-risk VMs.
• The system integrates VM security architecture with scheduling and detection modules for holistic protection.
• Functional testing is conducted to evaluate the model’s responsiveness, detection accuracy, and scalability.
• The approach combines decision-making (AHP), anomaly detection (CUSUM), and runtime adaptation (migration) into a unified framework.

Experimental results

Research questions

• RQ1 How can VM scheduling in cloud environments be optimized to enhance security and resource utilization?
• RQ2 To what extent can AHP-based decision-making improve the selection and placement of VMs in a secure manner?
• RQ3 Can the CUSUM algorithm effectively detect DDoS attacks in real time within a cloud virtualization environment?
• RQ4 How does dynamic VM state migration contribute to mitigating security threats in live cloud workloads?
• RQ5 What is the overall effectiveness of integrating AHP, CUSUM, and migration in a unified security management model?

Key findings

• The AHP-based scheduling method successfully prioritized VM deployment based on security and performance criteria, improving load balancing and risk-aware placement.
• The CUSUM-based DDoS detection algorithm demonstrated early and accurate identification of attack patterns, reducing false positives in dynamic traffic.
• Dynamic VM state migration enabled rapid response to detected threats, minimizing service disruption and containment time.
• Functional testing confirmed the model’s ability to maintain system stability under attack conditions while optimizing resource use.
• The integrated model showed enhanced resilience and adaptability in securing large-scale virtualized cloud environments.
• The combination of AHP, CUSUM, and migration provided a scalable and responsive security management framework for cloud workloads.