A Survivability Decision Model for Critical Information Systems Based on Bayesian Network

Critical information systems (CISs) cover vast number of applications and are now an essential part of our dayto-day life. Any damage to such a system or loss of information due to malicious attacks or system failures can cause serious consequences to society and individuals. Therefore, it is important to maintain the survivability of the systems and make timely decisions on system repair, if necessary, in order for the systems to support critical services. In this paper, we propose a Bayesian network based decision model to help system administrators better understand the hidden states of a CIS in order to determine its survivability status based on prior knowledge and current available evidence. We perceive that the survivability of a system is dependent on several factors in such a way that probabilistic relationships exist between these factors and the system’s survivability status. We represent such probabilistic relationships using a Bayesian network. Our model can be used to determine whether it is adequate for the system to continue supporting critical services or it needs to be repaired to avoid further losses. Therefore, the model helps system administrators to reduce the magnitude of possible service interruptions due to malicious attacks or system failures. 1