Conceptual Model of Risk Assessment for Insider Threats Detection

This study proposes a conceptual model of risk assessment for insider threats detection in Cyber-Physical system (CPs). The objectives of this research are two folds: a) finding the gap of study and b) produce a conceptual model of risk assessment for insider threats. This study has been conducted since an increasing number of cyber-attacks cases reported in CPs. In fact, the attack occurs unintentionally or intentionally from inside and outside of the organization due to the growth of new devices, sensors and mobile phones that connected to the network. However, to charge that the attack is coming from inside is more difficult since lack of evidence and involved cyber laws. Therefore, to detect insider threats demand new approach for better decision making. Current methods used for detecting insider threats are OCTAVE, FRAP, CRAMM, NIST, Monte Carlo and Markov Chain. Based on our findings, Monte Carlo and Markov Chain is a simulation-based method and applicable for organizations in terms of effective cost and system maintenance. The impact of the new model brings a better solution for assessing insider threats in organizations.