Survivability Model of Networks using Attack Graphs and Markov Chains

The most difficult task that a networks administrator faces today is to save a network from an ongoing cyber attack. Modern networks have the capability of detecting ongoing cyber attacks (i.e., using Intrusion Detection Systems (Ids)). Having said that, the number of false alarms tend to be high and acting upon every alarm does not necessarily make the network effective. In this paper, the contributions are-(a) established the credibility of an alarm using poisson distribution; and (b) presented a novel survivability model using the concepts of markov chains and attack graphs. The overall objective in this paper is to help network administrators to decide on whether to terminate a computer system in order to save the network (i.e., when an alarm is raised by an Ids).