A Framework for Realizing Security on Demand in Cloud Computing

Jamkhedkar, Pramod A.; Szefer, Jakub; Perez-Botero, Diego; Zhang, Tianwei; Triolo, Gina; Lee, Ruby B.

doi:10.1109/cloudcom.2013.55

Cited by 16 publications

(7 citation statements)

References 22 publications

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“…As such, the cost of the security measure needs to be paid by the client who has requested it. This scenario is adopted from Reference and can be expressed as follows:…”

Section: Proposed Cloud Migration Frameworkmentioning

confidence: 99%

Analysis of a cloud migration framework for offline risk assessment of cloud service providers

Sen

Madria

2020

Softw Pract Exp

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Generic notions of security on cloud platforms make clients apprehensive about fully migrating their applications on these platforms. The challenge lies in the capability of personalizing the security assessments of different cloud service providers from the perspective of the security requirements of the client applications to be hosted on them. This challenge was addressed by the previously proposed offline risk assessment framework for cloud service providers. This article presents a comprehensive analysis of a cloud migration framework that has been designed by adapting the novel security assessment principles of the offline risk assessment framework. The migration strategy has been modeled as a multiobjective optimization problem to further study the performance of numerous evolutionary algorithms in designing various cloud migration scenarios. The overall effectiveness of the proposed framework has been examined using a use-case application scenario and semisynthetic cloud service providers. K E Y W O R D Scloud migration, cloud security, genetic algorithm, multiobjective optimization, risk assessment, sensitivity analysis 1 998

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“…As such, the cost of the security measure needs to be paid by the client who has requested it. This scenario is adopted from Reference and can be expressed as follows:…”

Section: Proposed Cloud Migration Frameworkmentioning

confidence: 99%

Analysis of a cloud migration framework for offline risk assessment of cloud service providers

Sen

Madria

2020

Softw Pract Exp

View full text Add to dashboard Cite

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“…Cloud computing is the core technology of the next generation of network computing platform [Al Tayeb (2013)]. Jamkhedkar et al studied the security on demand in cloud computing [Jamkhedkar, Szefer, Perezbotero et al (2013)]. Garg et al [Garg, Versteeg, Buyya et al (2013)] shown a framework where customers' security requests can be expressed and enforced by leveraging the capabilities of servers with different security architectures.…”

Section: Related Workmentioning

confidence: 99%

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Li¹,

Shang²,

Zhang³

et al. 2019

Computers, Materials &Amp; Continua

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Pipeline defect detection systems collect the videos from cameras of pipeline robots, however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats. The existing systems tend to reach the limit in terms of data access anywhere, access security and video processing on cloud. There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection. In this paper, we deploy the framework of a cloud based pipeline defect detection system, including the user management module, pipeline robot control module, system service module, and defect detection module. In the system, we use a role encryption scheme for video collection, data uploading, and access security, and propose a hybrid information method for defect detection. The experimental results show that our approach is a scalable and efficient defection detection cloud system.

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“…Any VM thus created must satisfy the properties of security viz confidentiality, integrity, availability and authenticity. A VM's security health [12] is monitored for any malwares or guest OS root kits, any co resident VMs on the same server [5,6,10,11].…”

Section: B Security In Virtual Machinesmentioning

confidence: 99%

Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots

Joseph

Mukesh

2018

JCOMSS

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Abstract-Cloud Computing strives to be dynamic as a service oriented architecture (SoA). The services in the SoA are rendered in terms of private, public and in many other commercial domain aspects. These services should be secured and thus are very vital to the cloud infrastructure. In order, to secure and maintain resilience in the cloud, it not only has to have the ability to identify the known threats but also to new challenges that target the infrastructure of a cloud. In this paper, we introduce and discuss a detection method of malwares from the VM memory snapshot analysis and the corresponding VM snapshots are classified into attacked and non-attacked VM snapshots. As snapshots are always taken to be a backup in the backup servers, this approach could reduce the overhead of the backup server with a self-healing capability of the VMs in the local cloud infrastructure itself without any compromised VM in the backup server. A machine learning approach is projected here to classify the attacked and non attacked snapshots. The features of the snapshots are gathered from the API calls of VM instances. Our proposed scheme has a high detection accuracy of about 93% while having the capability to classify and detect different types of malwares with respect to the VM snapshots. Finally the paper exhibits an algorithm using snapshots to detect and thus to selfheal. The self-healing approach with machine learning algorithms can determine new threats with some prior knowledge of its functionality.

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A Framework for Realizing Security on Demand in Cloud Computing

Cited by 16 publications

References 22 publications

Analysis of a cloud migration framework for offline risk assessment of cloud service providers

Analysis of a cloud migration framework for offline risk assessment of cloud service providers

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Detection of Malware Attacks on Virtual Machines for a Self-Heal Approach in Cloud Computing using VM Snapshots

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