Security in IoT network based on stochastic game net model

The growing prevalence of Internet of Things (IoT) ushers itself with various security concerns. Being complex in nature, traditional security countermeasures cannot be applied directly to IoT networks. Addressing this problem, this paper aims to combine the capabilities of 2 traditional methods namely, game theory (GT) and stochastic Petri nets (SPN), such that the resultant model is compatible for complex IoT networks. Game theory does not have enough modeling capability to cope up with complexity of IoT networks. However, it has an advantage of providing a priori idea of attacker's actions and strategies with the help of Nash equilibrium. This information is used by administrators to devise appropriate action plan to detect and prevent attacks on network. On the other hand, SPN is a dynamic, scalable and probabilistic model, which overcomes the limitations of GT. Nevertheless, it is not able to compute best strategies (Nash equilibrium) of attacker. Therefore, this paper proposes stochastic game net (SGN)–based model for security in IoT, which combines the advantages of SPN and GT. The novelty of the work lies in the fact that this is the first attempt to define SGN for handling security issues in IoT. Simulations performed using OPNET tool show that SGN shows 5.94% and 5.91% improvement in terms of confidentiality, 6.4% and 8% improvement in terms of integrity, and 6.7% and 8.9% improvement in terms of availability over SPN and GT, respectively.