Crime Predictive Model in Cybercrime based on Social and Economic Factors Using the Bayesian and Markov Theories

Abstract

If financial institutions cannot detect incidents effectively, it cannot succeed in responding to incidents. This implies that the detection of incidents, is the most important aspect of incident response. A stochastic process with a first order dependence in discrete state and time is described as Markov chain, in the same way, Bayesian theory is a mathematical framework for reasoning and performing inference using probability. These two theories when based on socioeconomic factors can be used to predict cybercrime occurrence in Management Information Systems (MIS). The advancement of technology in banking has made banking business processes very convenient, but as the technology advances, cybercrimes of different nature emerges and equally at its peak. In as much as there are different measures already in place to combat these crimes, there still lies so many vulnerabilities which cannot be evitable in any information systems. Financial institutions need to develop predictive models that can be used to combat this cybercrime activities. In this paper, the application of Markov chain and Bayesian inference was used to analyze the nature of cybercrime and the probability of its occurrence, and the results were used to analyze the possibility of occurrence of cybercrimes based on the factors considered. � 2021 IEEE