Cyber risk prediction through social media big data analytics and statistical machine learning

Subroto, Athor; Apriyana, Andri

doi:10.1186/s40537-019-0216-1

Cited by 50 publications

(33 citation statements)

References 27 publications

(26 reference statements)

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“…The capacity to react to such new risks, depending on the continual advancement of modern techniques, to safeguard inherently dangerous systems would be one of the aims of continuing commerce under these conditions (Hadj-Mabrouk, 2019). There is a shortage of a particular product that sets necessary steps in establishing business continuation in health insurers that use contemporary e-business innovations, such as the World Wide Web, mobile computing, net banking, virtual infrastructures, and the latest e-business innovations (Subroto & Apriyana, 2019).…”

Section: Background To the E-business Modelsmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

Lakshmanan

Maheswari

2021

Ann Oper Res

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The main job of the e-business professional is to produce appropriate and trustworthy financial data. The solid internal controlling systems and the ethics and integrity of administration and staff depend significantly on the accuracy and relevancy of financial reports. This article demonstrates how Artificial Intelligence interacts with internal controlling systems creatively to assist managers in creating quality financial data by minimizing the risk of loss. A risk analysis approach for e-business is proposed in this article. Even though various kinds of research suggested in accountancy utilizing artificial intelligence, none of these proved explicitly how data risks were reduced via AI technology. The analysis benefits organizations save considerable expenses and losses by providing reliable financial data, helping managers decide effectively, and enhancing firms' efficiency. This article offers a concept of utilizing artificial intelligence to automate the removal of the vulnerability of internal monitoring systems by all types of businesses. It decreases the risk control, identification, and reporting quality through managing the risk of information reporting by offering the highest risk prediction accuracy of 89% and the precision ratio of 91% compared to the existing models.

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Section: Background To the E-business Modelsmentioning

confidence: 99%

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

Lakshmanan

Maheswari

2021

Ann Oper Res

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“…CPS architectures on the other hand represent a very broad concept [18]. A system must integrate these diverse concepts into a cognitive state for big data analytics and statistical machine learning to predict cyber risks [19]. But the design of big data systems for edge computing environments is challenging [20].…”

Section: Literature Review On Artificial Intelligence Cps and Predicmentioning

confidence: 99%

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

et al. 2020

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We explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.

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“…The limitation was that posting a clinical post may not be a significant criterion for early diagnosis of psychological disorders, as some people may be affected by mental illnesses before posting. Besides, to identify vulnerabilities, Subroto and Apriyana ( Subroto and Apriyana, 2019 ) offered an algorithmic model applying social media analytics and ML algorithms to protect cyber-attacks. Despite the highest accuracy for the model created by artificial neural networks, it was not scalable, having hardware limitations, and was tested only on a small sample of Twitter dataset.…”

Section: Classification Of the Selected Papersmentioning

confidence: 99%

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Abkenar

Kashani

Mahdipour

et al. 2021

Telematics and Informatics

123

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Cyber risk prediction through social media big data analytics and statistical machine learning

Cited by 50 publications

References 27 publications

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

RETRACTED ARTICLE: A hybrid approach for risk analysis in e-business integrating big data analytics and artificial intelligence

Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

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