A Game Theoretic Approach for Quantitative Evaluation of Strategic Interactions between Hacker's Motivations

The use of game theory has introduced new insights in quantitative evaluation of security and dependability. Currently, there is a wide range of useful game theoretic approaches to model the behavior of intelligent agents. However, it is necessary to revise these approaches if there is a society of hackers with significant diversity in their behaviors. In this paper, we introduce a novel approach to extend the basic ideas of using game theory to predict transition rates in stochastic models. The proposed method categorizes the society of hackers based on two main criteria used widely in hacker classification: motivations and skills. Markov chains are used to model the system. Based on the preferences of each class of hackers and the distribution of skills in each class, the transition rates between the states are computed. The resulting Markov chains can be solved to obtain the corresponding security measures of the system. We will also present the results of a case study using the proposed approach.