A big data framework for network security of small and medium enterprises for future computing

Kim, Hakyun; So, Won-Hyun; Je, Seung-Mo

doi:10.1007/s11227-019-02815-8

Cited by 24 publications

(17 citation statements)

References 32 publications

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“…Moreover, AI advances big data collection and processing, analysis, drives conclusions and provides recommendations (Drydakis, 2021;Jablonska & Polkowski, 2017;Bengio et al, 2013). AI is utilised by SMEs to support their business operations in areas such as sales and marketing, conversational commerce, customer service support, data analytics, sustainable development, credit evaluations, risk assessments, cybersecurity and legal services (Basri, 2021;Camacho-Miñano et al, 2015;Hamal & Senvar, 2021;Ikumoro & Jawad, 2019;Kim et al, 2019;Pérez et al, 2019;Rebón et al, 2015;Sood, 2020;Watney & Auer, 2021), helping to improve the quality optimisation, operational efficiencies and overall development of SMEs (Hansen & Bøgh, 2021;Kumar & Kalse, 2021).…”

Section: Applications Of Artificial Intelligence In Businessmentioning

confidence: 99%

Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

Drydakis

2022

Inf Syst Front

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The study utilises the International Labor Organization's SMEs COVID-19 pandemic business risks scale to determine whether Artificial Intelligence (AI) applications are associated with reduced business risks for SMEs. A new 10-item scale was developed to capture the use of AI applications in core services such as marketing and sales, pricing and cash flow. Data were collected from 317 SMEs between April and June 2020, with follow-up data gathered between October and December 2020 in London, England. AI applications to target consumers online, offer cash flow forecasting and facilitate HR activities are associated with reduced business risks caused by the COVID-19 pandemic for both small and medium enterprises. The study indicates that AI enables SMEs to boost their dynamic capabilities by leveraging technology to meet new types of demand, move at speed to pivot business operations, boost efficiency and thus, reduce their business risks.

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Section: Applications Of Artificial Intelligence In Businessmentioning

confidence: 99%

Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

Drydakis

2022

Inf Syst Front

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“…This better-connected industrial environment allows for superior information exchange between all the components involved [4]. Consequently, the IIoT paradigm is facilitating the development of more sophisticated technical solutions and autonomous software algorithms [5][6][7] that can improve the working characteristics (energy consumption, time efficiency) for industrial processes (see [8][9][10][11]).…”

Section: Industrial Internet Of Things (Iiot)mentioning

confidence: 99%

Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0

Nicolae

Korodi

Silea

2019

Water

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Current efforts towards achieving better connectivity and increasing intelligence in functioning of industrial processes are guided by the Industrial Internet of Things paradigm and implicitly stimulate occurrence of data accumulation. In recent years, several researchers and industrial products have presented Historian application solutions for data accumulation. The large amounts of data that are gathered by these Historians remains mostly unused or used only for reporting purposes. So far, Historians have been focused on connectivity, data manipulation possibilities, and sometimes on low-cost solutions in order to gain higher applicability or to integrate multiple SCADA servers (e.g. Siemens–WinCC, Schneider Electric – Vijeo Citect, IGSS, Wonderware, InduSoft Web Studio, Inductive Automation – Ignition, etc.), etc. Both literature and industry are currently unable to identify a Historian solution that functions in fog and efficiently applies and is built upon Industry 4.0 ideas. The future is to conceive a proactive Historian that is able to, besides gathering data, identify dependencies and patterns for particular processes and elaborate strategies to increase performance in order to provide feedback through corrective action on the functional system. Using available solutions, determining patterns by the Historian operator in the context of big data is a tremendous effort. The motivation of this research is provided by the currently unoptimized and partly inefficient systems in the water industry that can benefit from cost reduction and quality indicator improvements through IIoT concepts related to data processing and process adjustments. As the first part of more complex research to obtain a proactive Historian, the current paper wishes to propose a reference architecture and to address the issue of data dependency analyses as part of pattern identification structures. The conceptual approach targets a highly customizable solution considering the variety of industrial processes, but it also underlines basic software modules as generally applicable for the same reason. To prove the efficiency of the obtained solution in the context of real industrial processes, and their corresponding monitoring and control solutions, the paper presents a test scenario in the water industry.

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“…The main newly identified types of risks are as follows: Production (information risk associated with data loss, loss of integrity and available information), Maintenance (problems with the availability and integrity of maintenance data), Used Methods and Tools (data processing errors), Machines and Production Technologies (sensitivity and vulnerability of a cybernetic attack data problem), Human Resources (low number of qualified workers), Machine Environment (internet network attacks, electromagnetic compatibility issues affecting production machines). The IT sector uses an "Information Security Management System" (ISMS) structure, including administrative security, technical security and physical security (Kim et al 2019). The security of information is particularly related to the confidentiality of information.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Introducing Industry 4.0 to SMES

2019

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The aim of the paper is to identify some of the factors that affect the introduction of Industry 4.0 elements to small and medium-sized enterprises (SMEs). The article is concerned with factors that can be impulsive for SMEs and factors that, on the contrary, are limiting for SMEs to integrate Industry 4.0 into the enterprises. These factors are the result of a short brainstorming with some employees of 72 selected SMEs for case studies. The analysis of 1018 Czech SMEs showed that the introduction of Industry 4.0 is related to the size of the enterprise. Fisher’s Factorial Test based on a four-fold contingency table tested the data. The majority of medium-sized enterprises consider introducing digitization and robotization elements in the next 5 years, while in the case of micro-enterprises it was less than a half of the enterprises of the sample. At the same time, the relation between the enterprises with a written strategy and enterprises planning to implement Industry 4.0 was demonstrated.

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A big data framework for network security of small and medium enterprises for future computing

Cited by 24 publications

References 32 publications

Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic

Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0

Factors Introducing Industry 4.0 to SMES

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