A Method for Estimation of the Success Probability of an Intrusion Process by Considering the Temporal Aspects of the Attacker Behavior

Abstract

The aim is to propose a new approach for stochastic modeling of an intrusion process and quantitative evaluation of the probability of the attacker success. In many situations of security analysis, it is necessary to obtain the probabilities of success for attackers in an intrusion process. In the proposed method, the intrusion process is considered as elementary attack phases. In each atomic phase the attacker and the system interact and this interaction can transfer the current system state to a secure or failure state. Intrusion process modeling is done by a semi-Markov chain (SMC). The distribution functions assigned to the SMC transitions are a linear combination of some uniform distributions. These mixture distributions represent the time distribution of the attacker or the system in the transient states. In order to evaluate the security measure, the SMC is converted into a discrete-time Markov chain (DTMC) and then the resulting DTMC is analyzed and the probability of the attacker success is computed based on mathematical theorems. The desired security measure is evaluated with respect to the temporal aspects of the attacker behavior.