scholarly journals An Efficient Data Analysis Framework for Online Security Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jun Li ◽  
Yanzhao Liu
Keyword(s):  
Data Analysis ◽  
Data Security ◽  
Large Scale ◽  
Security Analysis ◽  
Analysis Framework ◽  
Online Data ◽  
Cloud Data ◽  
Computing Paradigm ◽  
Block Storage ◽  
Industrial Cloud

Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. LCN-Index uses the MapReduce computing paradigm to deploy large scale online data analysis rules: in the mapping stage, it divides each attribute into a batch of analysis predicate sets which are then deployed onto a mapping node using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multiattribute hash index. By doing so, a stream tuple can be efficiently evaluated by going over the LCN-Index framework. Experiments demonstrate the utility of the proposed method.

SAE: Toward Efficient Cloud Data Analysis Service for Large-Scale Social Networks

2017 ◽  
Vol 5 (3) ◽  
pp. 563-575 ◽  
Author(s):  
Yu Zhang ◽  
Xiaofei Liao ◽  
Hai Jin ◽  
Guang Tan
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Large Scale ◽  
Cloud Data

scholarly journals Automated Clustering of Virtual Machines based on Correlation of Resource Usage

2012 ◽  
Vol 8 (4) ◽  
pp. 102 ◽  
Author(s):  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Virtual Machines ◽  
Resource Usage ◽  
Cloud Data ◽  
Multiple Resources ◽  
Computing Paradigm ◽  
Innovative Methodology ◽  
Cloud Data Centers

The recent growth in demand for modern applicationscombined with the shift to the Cloud computing paradigm have led to the establishment of large-scale cloud data centers. The increasing size of these infrastructures represents a major challenge in terms of monitoring and management of the system resources. Available solutions typically consider every Virtual Machine (VM) as a black box each with independent characteristics, and face scalability issues by reducing the number of monitored resource samples, considering in most cases only average CPU usage sampled at a coarse time granularity. We claim that scalability issues can be addressed by leveraging thesimilarity between VMs in terms of resource usage patterns.In this paper we propose an automated methodology to cluster VMs depending on the usage of multiple resources, both systemand network-related, assuming no knowledge of the services executed on them. This is an innovative methodology that exploits the correlation between the resource usage to cluster together similar VMs. We evaluate the methodology through a case study with data coming from an enterprise datacenter, and we show that high performance may be achieved in automatic VMs clustering. Furthermore, we estimate the reduction in the amount of data collected, thus showing that our proposal may simplify the monitoring requirements and help administrators totake decisions on the resource management of cloud computing datacenters.

scholarly journals An efficient data security mechanism with data sharing and authentication

2020 ◽  
Vol 5 (19) ◽  
pp. 26-31
Author(s):  
Md. Farooque ◽  
Kailash Patidar ◽  
Rishi Kushwah ◽  
Gaurav Saxena
Keyword(s):  
Cloud Computing ◽  
Data Sharing ◽  
Data Security ◽  
Security Analysis ◽  
Key Generation ◽  
Security Mechanism ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Efficient Data

In this paper an efficient security mechanism has been adopted for the cloud computing environment. It also provides an extendibility of cloud computing environment with big data and Internet of Things. AES-256 and RC6 with two round key generation have been applied for data and application security. Three-way security mechanism has been adopted and implemented. It is user to user (U to U) for data sharing and inter cloud communication. Then user to cloud (U to C) for data security management for application level hierarchy of cloud. Finally, cloud to user (C to U) for the cloud data protection. The security analysis has been tested with different iterations and rounds and it is found to be satisfactory.

scholarly journals A Two Way Validation Framework for Cloud Storage Security

2018 ◽  
Vol 7 (2.20) ◽  
pp. 236
Author(s):  
Anantula Jyothi ◽  
Baddam Indira
Keyword(s):  
Data Security ◽  
High Performance ◽  
Large Scale ◽  
Substantial Reduction ◽  
Audit Process ◽  
Application Performance ◽  
Storage Security ◽  
Cloud Data ◽  
Data Auditing ◽  
Computational Resources

High Performance Computing (HPC) has become one of the predominant techniques for processing the large scale applications. Cloud environment has been chosen to provide the required services and to process these high demand applications. Management of such            applications challenges us on three major things i.e. network feasibility, computational feasibility and data security. Several research endeavours are focused on network load and computing cloud date and provided better outcomes. Still those approaches are not able to provide standard mechanisms in view of data security. On the other side, research towards enabling the auditing features on the cloud based data by various researchers has been addressed but their performance is poor. However, the complexity of the audit process proven to be the bottleneck in improving performance of the application as it consumes the computational resources of the same application. Henceforth, this work proposes a novel framework for cloud data auditing at multiple levels to audit the access requests and upon             validating the conditions of one level, the connection request will be moved to the further complex levels in order to reduce the              computational loads. The proposed framework determines a substantial reduction in the computational load on the cloud server, thus improves the application performance leveraging the infrastructure use. 

scholarly journals Blockchain-Based Cloud Data Integrity Verification Scheme with High Efficiency

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Gaopeng Xie ◽  
Yuling Liu ◽  
Guojiang Xin ◽  
Qiuwei Yang
Keyword(s):  
Cloud Storage ◽  
Large Scale ◽  
High Efficiency ◽  
Security Analysis ◽  
Data Integrity ◽  
Verification Scheme ◽  
Cloud Data ◽  
Integrity Verification ◽  
Computational Overhead ◽  
Cuckoo Filter

With the large-scale application of cloud storage, how to ensure cloud data integrity has become an important issue. Although many methods have been proposed, they still have their limitations. This paper improves some defects of the previous methods and proposes an efficient cloud data integrity verification scheme based on blockchain. In this paper, we proposed a lattice signature algorithm to resist quantum computing and introduced cuckoo filter to simplify the computational overhead of the user verification phase. Finally, the decentralized blockchain network is introduced to replace traditional centralized audit to publicize and authenticate the verification results, which improves the transparency and the security of this scheme. Security analysis shows that our scheme can resist malicious attacks and experimental results show that our scheme has high efficiency, especially in the user verification phase.

Control Cloud Data Access Privilge Anonymity with Attributed Based Encryption

2017 ◽  
Vol 7 (8) ◽  
pp. 279
Author(s):  
P. Sudheer ◽  
T. Lakshmi Surekha
Keyword(s):  
Data Privacy ◽  
Low Cost ◽  
Data Access ◽  
Privacy Concerns ◽  
Cloud Data ◽  
Computing Paradigm ◽  
Attribute Based Encryption ◽  
Data Content ◽  
Identity Privacy ◽  
Cloud Servers

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been to secure the cloud storage. Data content privacy. A semi anonymous privilege control scheme AnonyControl to address not only the data privacy. But also the user identity privacy. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity. The  Anonymity –F which fully prevent the identity leakage and achieve the full anonymity.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

Sign in / Sign up

Export Citation Format

Share Document