A Cryptography and Machine Learning Based Authentication for Secure Data-Sharing in Federated Cloud Services Environment

Singh, Ashutosh Kumar; Saxena, Deepika

doi:10.1080/19361610.2020.1870404

Cited by 27 publications

(20 citation statements)

References 37 publications

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“…To address these issues, robust mutual authentication is required for secure key exchange and online data sharing [25], [26], [27], [28]. This also ensures that there are unique access control procedures and session key establishment before the onset of data sharing, especially in federated cloud environment [29]. Apart from authentication, techniques such as protected credential storage, secure secret key sharing, secure data storage and data sharing have been identified in [30], [31], [32], [33], [34], [35], [36].…”

Section: Figure 1 Iot-cloud Environmentmentioning

confidence: 99%

“…On its part, the protocol in [80] cannot offer untraceability and anonymity [81]. Based on ensemble voting classifier, ECC and Schnorr's signature, a mutual authentication scheme is developed in [29] to detect and mitigate security breaches. On the other hand, the protocol in [82] is defenseless against secret key guessing attacks.…”

Section: Security Schemes For Cloud Environmentmentioning

confidence: 99%

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Cloud computing communication environment security and performance: Challenges and probable solutions

Kanini¹

2023

World J. Adv. Eng. Technol. Sci.

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The adoption of cloud computing has continued to grow, owing to its broad network access, remote accessibility, scalability, on demand self-service, rapid elasticity and resource pooling. In this environment, numerous devices are deployed to access the cloud data and services. This potentially increases the attack services, more so in public clouds. As such, the biggest challenge is the secure data exchange over insecure open network channels. This issue has seen the development of numerous security solutions over the recent past. In this paper, a survey of these security schemes, techniques and methods is provided. The findings indicate that majority of these methods have vulnerabilities that expose users to many attacks. Additionally, some of these security solutions have extremely high complexities that make the cloud communications inefficient.

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Section: Figure 1 Iot-cloud Environmentmentioning

confidence: 99%

Section: Security Schemes For Cloud Environmentmentioning

confidence: 99%

Cloud computing communication environment security and performance: Challenges and probable solutions

Kanini¹

2023

World J. Adv. Eng. Technol. Sci.

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“…This problem is formulated using Eqs. ( 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%

“…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%

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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“…The comprehensive working of datacenters depend on fine-grained provisioning of resources like storage, processor and network etc. [6], [7]. The workload demands show high variation over the time causing over/under-load and SLA violation problems where the static allocation of resources is unapplicable [8].…”

Section: Introductionmentioning

confidence: 99%

A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

Singh

Saxena

Kumar

et al. 2021

IEEE Trans. Parallel Distrib. Syst.

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This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects of the Controlled-NOT (C-NOT) gate serve activation function at the hidden and output layers to adjust the qubit weights. In addition, a Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize qubit network weights. The accuracy of the EQNN prediction model is extensively evaluated and compared with seven state-of-the-art methods using eight real world benchmark datasets of three different categories. Experimental results reveal that the use of the quantum approach to evolutionary neural network substantially improves the prediction accuracy up to 91.6% over the existing approaches.

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A Cryptography and Machine Learning Based Authentication for Secure Data-Sharing in Federated Cloud Services Environment

Cited by 27 publications

References 37 publications

Cloud computing communication environment security and performance: Challenges and probable solutions

Cloud computing communication environment security and performance: Challenges and probable solutions

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

A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

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