MLPAM: A Machine Learning and Probabilistic Analysis Based Model for Preserving Security and Privacy in Cloud Environment

Gupta, Ishu; Gupta, Rishabh; Singh, Anurag; Buyya, Rajkumar

doi:10.1109/jsyst.2020.3035666

Cited by 52 publications

(22 citation statements)

References 29 publications

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“…Machine Learning and Probabilistic Analysis based Model (MLPAM) is a novel model created with machine learning to preserve data in the Cloud environment. A comprehensive analysis indicates its security and efficiency in [308], the authors have argued that MLPAM could provide safety and efficiency of data sharing and control in the future for multiple environments such as IoT while in [309], An Automatic Cloud Detection neural network (ACD net) has been presented that contains two features which can overcome the overestimation problems in Cloud Computing. However, the authors have not created a future road map and explain that their solution cannot overcome the thin cloud detection issue because of an inadequate training dataset.…”

Section: Ai In Cloudmentioning

confidence: 99%

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Ahmadi¹,

Salehfar²

2022

Preprint

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Privacy-Preserving Cloud Computing is an emerging technology with many applications in various fields. Cloud computing is important because it allows for scalability, adaptability, and improved security. Likewise, privacy in cloud computing is important because it ensures that the integrity of data stored on the cloud maintains intact. This survey paper on privacy-preserving cloud computing can help pave the way for future research in related areas. This paper helps to identify existing trends by establishing a layered architecture along with a life cycle and an ecosystem for privacy-preserving cloud systems in addition to identifying the existing trends in research on this area.

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Section: Ai In Cloudmentioning

confidence: 99%

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Ahmadi¹,

Salehfar²

2022

Preprint

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“…( 16) and (17) where ψ represents allocation of Q VMs to P servers such that objectives: P W , ϑ, φ are minimized and RU is maximized subject to constraints specified in Eqs. ( 12)- (15).…”

Section: F Constraintsmentioning

confidence: 99%

“…The steps 6-14 specify that each allocation must satisfy the resource capacity constraints mentioned in Eqs. ( 12)- (15). The steps 17-19 remove unallocated VMs from k th solution (ψ k ) and refill them by applying FFD algorithm having time complexity: O(XP Q).…”

Section: B Operational Design and Complexity Computationmentioning

confidence: 99%

“…E-mail: 13deepikasaxena@gmail.com, ishugupta23@gmail.com, jitendra@nitt.edu, ashutosh@nitkkr.ac.in X. Wen is with the Department of Creative Informatics and the Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka 8208502, Japan (e-mail: wen@cse.kyutech.ac.jp) [12], [13], [14]. Generally, users are completely unaware about the actual location of their VMs and run their confidential applications ignoring the probability of security attacks [15], [16], [17]. Infact, co-residency raises occurrence of security threats by giving extensive opportunity to the attacker to exploit vulnerabilities of hypervisor and compromise other user's VM [18].…”

Section: Introductionmentioning

confidence: 99%

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A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

Saxena,

Gupta,

Kumar

et al. 2021

Preprint

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To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.

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“…It drastically transforms the means of computing. It allows users to leverage the computing resources for the required time on the "pay-as-you-go" model [22]- [24]. Cloud computing also makes it possible to access resources from anywhere, while in the traditional computer system, you have to be there, where the physical resource is located [25]- [27].…”

Section: Introductionmentioning

confidence: 99%

A Discussion and Comparative Study on Security and Privacy of Smart Meter Data

Kumar¹,

Singh²

2021

Preprint

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Cloud computing comes with a lot of advanced features along with privacy and security problem. Smart meter data takes the benefit of cloud computing in the smart grid. User's privacy can be compromised by analyzing the smart meter data generated by household electrical appliances. The user loses control over the data while data is shifted to the cloud. This paper describes the issues under the privacy and security of smart meter data in the cloud environment. We also compare the existing approaches for preserving the privacy and security of smart meter data.

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MLPAM: A Machine Learning and Probabilistic Analysis Based Model for Preserving Security and Privacy in Cloud Environment

Cited by 52 publications

References 29 publications

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

A Discussion and Comparative Study on Security and Privacy of Smart Meter Data

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