A Knowledge-Based Bayesian Model for Analyzing a System after an Insider Attack

Many consider insider attacks to be more severe than outsider attacks due to the nature of such attacks that involve people who have knowledge of their own organization. In this work, we presented a new model to evaluate and analyze a system after the occurrence of an insider attack. By evaluating and analyzing the system after detecting such attack, we classified systems’ objects into a list of non affected objects and a list of affected objects. We also introduced a new graph called knowledge Bayesian attack graph (KBAG). KBAG represents possible candidate paths that malicious insiders may follow to achieve their goal of compromising critical objects. KBAG also enables us to calculate risk values for different objects using Bayesian inference techniques. These risk values will be considered as measurements for the likelihood of possible occurrence of other insider attacks that have not yet been detected by the underlying system.