Modelling Adversarial Risk in Big Data

This paper seeks to develop methods to support decisions in relation to adversarial risk analysis of big data by particularly determining some adversarial risk estimators to be derived from big data analysis using the adversarial risk analysis structural equation modelling (ARA-SEM). Data was simulated for one thousand (1000) observations with the results revealing 19 iterative solutions to the latent and measurement models with 16 possessing adversarial risks. The paper recommends the fitting of the ARA-SEM model based on the statistically significant adversarial risk presence as given by the latent and measurement model outcomes.