An Optimal Sanitization Algorithm Based Secure Migration of Virtual Machines in Cloud Datacenters

John, Nimmol P.; Jv.R, Bindu

doi:10.21817/indjcse/2021/v12i3/211203229

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Renuga investigated data remnants after consumers accidentally/intentionally deleted their data [32] and Han proposed a transparent data sanitization approach for data confidentiality [33]. The approach is implemented as an internet gateway within the premises of the consumer organization using a JavaScript injecting technique that sanitizes sensitive data.…”

Section: Data Security Measuresmentioning

confidence: 99%

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Cloud Security Using Fine-Grained Efficient Information Flow Tracking

Alqahtani,

Almutairi,

Sheldon

2024

Future Internet

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This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework.

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Section: Data Security Measuresmentioning

confidence: 99%

“…The system is tested via a framework prepared to deploy IFT-aware web applications in the cloud. John investigated an IFT system that simplifies data protection for tenants and PaaS providers [33]. The results suggest that DIFT is suitable for protecting data integrity and privacy in PaaS platforms.…”

Section: Studies Using Ift For Cloud Securitymentioning

confidence: 99%

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

Alqahtani,

Almutairi,

Sheldon

2024

Future Internet

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Gradient Beetle Honey Badger Optimization based container migration and optimal key selection using Firefly Honey Badger Algorithm in cloud computing

Soma

2023

2023 Global Conference on Information Technologies and Communications (GCITC)

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Secure Virtual Machine Live Migration using Advanced Metric Encryption

Saravanaguru

Narayanan

2023

RACSC

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Cloud is based on the underlying technology of virtualization. Here, the physical servers are divided into multiple virtual servers. Through the technology of virtualization, each virtual server contains virtual machines. Live virtual machine migration is expected to be with the aim of having migration time and inactive time with minimal duration. Various machine-learning approaches have been investigated and identified research gaps to enhance the security features during the migration process. Moreover, a secure virtual machine live migration is proposed using Advanced Metric Encryption (AME). Considering the duration of live migration in data centers as well as ensuring the security aspects, the proposed model has been tested and evaluated.

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An Optimal Sanitization Algorithm Based Secure Migration of Virtual Machines in Cloud Datacenters

Cited by 3 publications

References 16 publications

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

Gradient Beetle Honey Badger Optimization based container migration and optimal key selection using Firefly Honey Badger Algorithm in cloud computing

Secure Virtual Machine Live Migration using Advanced Metric Encryption

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