A generic scheme for cyber security in resource constraint network using incomplete information game.

We propose an online learning model that efficiently teaches a defender's agent to learn the attacker's behavior while interacting in the cyber-world. This paper models the interaction between these two agents as a stochastic game with limited rationality. Because of this limited rationality, the proposed model helps the defender's agent learn the unknown communicator's behavior from the feedback obtained while interacting with it. Many models are built to solve the interaction between them by developing a state-oriented stochastic Markov game. However, such models fail due to the state explosion problem, and therefore, this paper discusses a model to solve this game, restricting it to a stateless stochastic game. The model is then compared to check the performance with different algorithms that solve stochastic games. The comparison between them shows that the proposed algorithm converges to an optimal strategy in a brief simulation time span. Finally, our model checks the performance with an existing technique that shows that the proposed algorithm chooses the correct strategy for around 91% of the simulation time compared to 73% of the simulation time by the existing algorithm. [ABSTRACT FROM AUTHOR]