Factors Comprising Effective Risk Communication, Decision-Making, and Measurement of IT and IA Risk

Daza, Ricardo; Hargiss, Kathleen M.

doi:10.4018/ijsita.2018010102

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…When the distance between the primary user and the auxiliary user is less than the preset distance threshold, MD and SD can establish a data transmission link through the device through technology. The channel model not only considers the path loss based on distance but also considers smallscale fading [18,19]. The international financial mechanism refers to the general term for the rules, conventions, policies, mechanisms, and organizational arrangements for the regulation of international payment, settlement, exchange, and transfer of various currencies.…”

Section: Financial Asset Risk Measurement Experimentsmentioning

confidence: 99%

Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model

2022

Wireless Communications and Mobile Computing

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Following the speeding up of a process of financial globalization, the risks faced by financial markets have become more complex and diversified. Correlated patterns among financial assets exhibit characteristics of nonlinearity, asymmetry, and tail correlation. The original linear correlation analysis method is no longer suitable, but relevant information describing financial risks. In order to confirm whether an asset is safe, the key is to study and master its volatility, and this is based on our mastery of volatility measurement skills. This article is based on smart sensor big data security analysis and Bayesian analysis. The risk measurement of financial assets based on the empirical probability model is studied. The GARCH- t (1,1) model is selected according to the Akaike information criterion (AIC) after the generalized autoregressive conditional heteroskedasticity (GARCH) model is established by the EViews software. According to the results of probability integral transformation, a series of correlation coefficients and degrees of freedom of t -copula are obtained by the maximum likelihood estimation method. This paper uses the risk-adjusted return on capital (RAROC) method to evaluate the risk performance of financial assets. Financial institutions can only retain and absorb the financial market risks that cannot be avoided and transferred. The edge user node sends the service request to the edge server node. The edge server uses the model proposed in this paper to evaluate the user’s trust and selects the corresponding service level according to the trust level corresponding to the calculated credibility results. The data show that the edge calculation takes 0.2581 seconds, while the linear search takes about 64 seconds. The results show that intelligent edge computing improves the accuracy and efficiency of financial asset risk measurement.

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Section: Financial Asset Risk Measurement Experimentsmentioning

confidence: 99%

Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model

2022

Wireless Communications and Mobile Computing

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“…In literature, IT systems were investigated as a whole entity where risk management is applied. Daza et al, (2018) focuses on factors that comprise effective risk communication, decisionmaking, and measurement of information technology (IT) and information assurance (IA) risk. They involve both IT/IA practitioners and recipients of risk communication through the identification of factors that influence IT/IA professionals.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Graph for Improvement of Decision-Making in Risks Treatment

Messaoudi

Dekhici

Noureddine

2022

International Journal of Decision Support System Technology

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Failures that hinder the proper functioning of complex systems are characterised by frequencies of occurrence, rates of evolution and variables periodicities. Indeed, a good design of this kind of systems requires the elaboration of an adapted analysis and modelling approach, which takes into account the spatial-temporal dynamics of the systems elements dysfunction. This paper proposes a Spatio-temporal modelling approach integrated into a risk management methodology. This modelling allows both to describe the evolution of the system in space and in time, and to follow the spatial and temporal scope of a failure of the system in order to improve decision making in choosing the appropriate corrective action. The elements of the methodology are illustrated and validated by a case study of a wireless sensor network system.

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Factors Comprising Effective Risk Communication, Decision-Making, and Measurement of IT and IA Risk

Cited by 2 publications

References 41 publications

Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model

Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model

Spatio-Temporal Graph for Improvement of Decision-Making in Risks Treatment

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