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.

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 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.

Using Big Data Analytics to Assist a Smart City to Prevent Cyber Security Threats

2021 ◽  
pp. 107-124
Author(s):  
Fenio Annansingh
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Smart City ◽  
Data Analytics ◽  
Life Quality ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Smart Services ◽  
Prevention Model

The concept of a smart city as a means to enhance the life quality of citizens has been gaining increasing importance in recent years globally. A smart city consists of city infrastructure, which includes smart services, devices, and institutions. Every second, these components of the smart city infrastructure are generating data. The vast amount of data is called big data. This chapter explores the possibilities of using big data analytics to prevent cybersecurity threats in a smart city. It also analyzed how big data tools and concepts can solve cybersecurity challenges and detect and prevent attacks. Using interviews and an extensive review of the literature have developed the data analytics and cyber prevention model. The chapter concludes by indicating that big data analytics allow a smart city to identify and solve cybersecurity challenges quickly and efficiently.

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

scholarly journals Key Success Factors of Information Systems Security

2019 ◽  
Vol 43 (2) ◽  
pp. 131-144
Author(s):  
Krunoslav Arbanas ◽  
Nikolina Žajdela Hrustek
Keyword(s):  
Information Systems ◽  
Information Security ◽  
Security Policy ◽  
Success Factors ◽  
Organizational Structures ◽  
Added Value ◽  
Management Support ◽  
Information Systems Security ◽  
Key Success Factors ◽  
Systems Security

The issue of information systems security, and thus information as key resource in today's information society, is something that all organizations in all sectors face in one way or another. To ensure that information remain secure, many organizations have implemented a continuous, structured and systematic security approach to manage and protect an organization's information from undermining individuals by establishing security policies, processes, procedures, and information security organizational structures. However, despite this, security threats, incidents, vulnerabilities and risks are still raging in many organizations. One of the main causes of this problem is poor understanding of information systems security key success factors. Identifying and understanding of information security key success factors can help organizations to manage how to focus limited resources on those elements that really impact on success, therefore saving time and money and creating added value and further enabling operational business. This research, based on comprehensive literature review, summarizes most cited key success factors of information systems security identified in scientific articles indexed in relevant databases, of which the top three success factors were management support, information security policy and information security education, training and awareness. At the end, article states identified research gaps and provides readers with possible directions for further researches

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