scholarly journals Which Aspects of Big Data Usage is Creating Information Security Concern?

2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Hira Batool ◽  
DU Rong
Keyword(s):  
Big Data ◽  
Information Security ◽  
Research Study ◽  
Big Data Analytics ◽  
Online Behavior ◽  
Security Issue ◽  
Data Usage ◽  
Theoretical Support ◽  
Proposed Model ◽  
Security Concern

The present research study proposed some of the big data usage perspective for testing either they have role in creating information security concern or not. The researchers first dig out some of the theoretical support for filling the gap regarding big data and information security bridge that was previously noted in the literature. The present researches approached big data analytics manager in the Pakistani banking industries for validating the proposed model. The Data was analyzed by using SPSS Andrews approach due to the nature of the research study. The findings revealed that proposed perspective including perceived benefits, cloud storage and online behavior monitoring should be test in the future studies by proposing their indirect affect in the creation of information security issue. The study brings new aspect in the literature of management regarding big data usage practices.

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

scholarly journals Ontology-based semantic similarity to metadata analysis in the information security domain

2021 ◽  
pp. 034-041
Author(s):  
A.Y. Gladun ◽  
◽  
K.A. Khala ◽  
Keyword(s):  
Big Data ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Network Attacks ◽  
Semantic Level ◽  
Operational Information ◽  
Metadata Analysis ◽  
Data Elements ◽  
Different Levels

It is becoming clear with growing complication of cybersecurity threats, that one of the most important resources to combat cyberattacks is the processing of large amounts of data in the cyber environment. In order to process a huge amount of data and to make decisions, there is a need to automate the tasks of searching, selecting and interpreting Big Data to solve operational information security problems. Big data analytics is complemented by semantic technology, can improve cybersecurity, and allows you to process and interpret large amounts of information in the cyber environment. Using of semantic modeling methods in Big Data analytics is necessary for the selection and combination of heterogeneous Big Data sources, recognition of the patterns of network attacks and other cyber threats, which must occur quickly to implement countermeasures. Therefore to analyze Big Data metadata, the authors propose pre-processing of metadata at the semantic level. As analysis tools, it is proposed to create a thesaurus of the problem based on the domain ontology, which should provide a terminological basis for the integration of ontologies of different levels. To build a thesaurus of the problem, it is proposed to use the standards of open information resources, dictionaries, encyclopedias. The development of an ontology hierarchy formalizes the relationships between data elements that will be used in future for machine learning and artificial intelligence algorithms to adapt to changes in the environment, which in turn will increase the efficiency of big data analytics for the cybersecurity domain.

Impact of Big Data on Security

2018 ◽  
pp. 283-299 ◽  
Author(s):  
Ramgopal Kashyap ◽  
Albert D. Piersson
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Privacy Preservation ◽  
Big Data Analytics ◽  
Security Issue ◽  
Social Media Data ◽  
Advantages And Disadvantages ◽  
Data Heterogeneity ◽  
Types Of Information ◽  
Media Data

The motivation behind this chapter is to highlight the qualities, security issue, advantages, and disadvantages of big data. In the recent researches, the issue and challenges are due to the exponential growth of social media data and other images and videos. Big data security threats are rising, which is affecting the data heterogeneity adaptability and privacy preservation analytics. Big data analytics helps cyber security, but no new application can be envisioned without delivering new types of information, working on data-driven calculations and expending determined measure of information. This chapter demonstrates how innate attributes of big data are protected.

Big Data Analytics Adoption Factors in Improving Information Systems Security

2022 ◽  
pp. 1231-1248
Author(s):  
Marouane Balmakhtar ◽  
Scott E. Mensch
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Information Systems Security ◽  
Systems Security ◽  
Security Effectiveness ◽  
Research Questions

This research measured determinants that influence the willingness of IT/IA professionals to recommend Big Data analytics to improve information systems security in an organization. A review of the literature as well as the works of prior researchers provided the basis for formulation of research questions. Results of this study found that security effectiveness, organizational need, and reliability play a role in the decision to recommend big data analytics to improve information security. This research has implications for both consumers and providers of big data analytics services through the identification of factors that influence IT/IA professionals. These factors aim to improve information systems security, and therefore, which service offerings are likely to meet the needs of these professionals and their organizations.

An independent time optimized hybrid infrastructure for big data analytics

2020 ◽  
Vol 34 (28) ◽  
pp. 2050311
Author(s):  
Satvik Vats ◽  
B. B. Sagar
Keyword(s):  
Big Data ◽  
Execution Time ◽  
Resource Sharing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Demilitarized Zone ◽  
Proposed Model ◽  
Hadoop Distributed File System ◽  
Improved Performance ◽  
Data Independence

In Big data domain, platform dependency can alter the behavior of the business. It is because of the different kinds (Structured, Semi-structured and Unstructured) and characteristics of the data. By the traditional infrastructure, different kinds of data cannot be processed simultaneously due to their platform dependency for a particular task. Therefore, the responsibility of selecting suitable tools lies with the user. The variety of data generated by different sources requires the selection of suitable tools without human intervention. Further, these tools also face the limitation of recourses to deal with a large volume of data. This limitation of resources affects the performance of the tools in terms of execution time. Therefore, in this work, we proposed a model in which different data analytics tools share a common infrastructure to provide data independence and resource sharing environment, i.e. the proposed model shares common (Hybrid) Hadoop Distributed File System (HDFS) between three Name-Node (Master Node), three Data-Node and one Client-node, which works under the DeMilitarized zone (DMZ). To realize this model, we have implemented Mahout, R-Hadoop and Splunk sharing a common HDFS. Further using our model, we run [Formula: see text]-means clustering, Naïve Bayes and recommender algorithms on three different datasets, movie rating, newsgroup, and Spam SMS dataset, representing structured, semi-structured and unstructured, respectively. Our model selected the appropriate tool, e.g. Mahout to run on the newsgroup dataset as other tools cannot run on this data. This shows that our model provides data independence. Further results of our proposed model are compared with the legacy (individual) model in terms of execution time and scalability. The improved performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model.

scholarly journals Eco-Innovation and Industry 4.0: A Big Data Usage conceptual model

2018 ◽  
Vol 56 ◽  
pp. 05003 ◽  
Author(s):  
Russell Tatenda Munodawafa ◽  
Satirenjit Kaur Johl
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Digital Economy ◽  
Future Research ◽  
New Era ◽  
Data Usage ◽  
The World ◽  
Development Goals

Driven by Cyber Physical Systems, Big Data Analytics, Internet of Things and Automation, Industry 4.0 is expected to revolutionize the world. A new era beckons for enterprises of all sizes, markets, governments, and the world at large as the digital economy fully takes off under Industry 4.0. The United Nations has also expressed its desire to usher in a new era for humanity with the Sustainable Development Goals 2030 (SDG’s) replacing the Millennial Development Goals (MDG’s). Critical to the achievement of both of the above-mentioned ambitions is the efficient and sustainable use of natural resources. Big Data Analytics, an important arm of Industry 4.0, gives organizations the ability to eco-innovate from a resource perspective. This paper conducts an analysis of previously published research literature and contributes to this emerging research area looking at Big Data Usage from a strategic and organizational perspective. A conceptual framework that can be utilized in future research is developed from the literature. Also discussed is the expected impact of Big Data Usage towards firm performance, particularly as the world becomes more concerned about the environment. Data driven eco-innovation should be in full motion if organizations are to remain relevant in tomorrow’s potentially ultra-competitive digital economy.

Big Data Analytics Adoption Factors in Improving Information Systems Security

2019 ◽  
Vol 10 (3) ◽  
pp. 1-21
Author(s):  
Marouane Balmakhtar ◽  
Scott E. Mensch
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Information Systems Security ◽  
Systems Security ◽  
Security Effectiveness ◽  
Research Questions

This research measured determinants that influence the willingness of IT/IA professionals to recommend Big Data analytics to improve information systems security in an organization. A review of the literature as well as the works of prior researchers provided the basis for formulation of research questions. Results of this study found that security effectiveness, organizational need, and reliability play a role in the decision to recommend big data analytics to improve information security. This research has implications for both consumers and providers of big data analytics services through the identification of factors that influence IT/IA professionals. These factors aim to improve information systems security, and therefore, which service offerings are likely to meet the needs of these professionals and their organizations.

scholarly journals Data Analytics to Examine Trending Topics for Indonesian Election 2019

2019 ◽  
Vol 4 (2) ◽  
pp. 235
Author(s):  
Firman Arifin ◽  
Budi Nur Iman ◽  
Budi Nur Iman ◽  
Elly Purwantini ◽  
Elly Purwantini ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Public Interest ◽  
Data Analytics ◽  
Political Competition ◽  
Big Data Analytics ◽  
Model Based ◽  
Political Campaigning ◽  
Proposed Model ◽  
Source Of Information

Understanding public interest and opinion are necessary tasks in high intense political competition. Utilizing big data analytics from social media provide an important source of information that candidates can utilize, manage and even engage them in targeted political campaigning agenda. One of the source in big data is social media’s interactions. Social media empowers public to participate proactivelyin the campaigning activities. This paper examines trends gathered from data analytics of two contenders’ group for Indonesian Election in 2019. It tracks the recent patterns of people engagement via social media analytic specifically Twitter. The study developed the analysis into the proposed model based on their trends and patterns.

scholarly journals Big data for small businesses: Abstracting security and decision-making tools

10.29007/6tpw ◽  
2019 ◽  
Author(s):  
Sara Salih ◽  
Kennedy Njenga
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Security ◽  
Small Businesses ◽  
Theoretical Underpinning ◽  
Substantive Theory ◽  
Business Decision ◽  
Data Usage ◽  
The Relationship ◽  
Financial Consideration

The study delineates the understanding of big data as an emergent phenomenon that has brought a notable shift in the relationship between technology and business decision- making. Using grounded theory techniques, the study espouses opportunities and alternative perceptions from small businesses regarding the value that big data may offer in contrast to usage experience by big businesses. Information security lies at the heart of these consideration. The study draws on concepts and tenets from the discipline of information security to support a theoretical underpinning for big data usage in small businesses. A substantive theory has been developed from this work with three distinct concepts emerging that show that financial consideration, management mindset and size consideration play a big part in influencing small business perceptions.

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