Extended game theoretic dirichlet based collaborative intrusion detection systems

Abstract

Security has always been one of the key issues of any man-made system, this paved the way for a submodule or application or a device to monitor or system for malicious activities. This system or submodule or device is known as Intrusion Detection System (IDS). As technology evolves so does the associated threats and thus the intrusion detection system needs to evolve. Game theory throws in a different perspective which have not been looked upon much. Game theory provides a way of mathematically formalizing the decision making process of policy establishment and execution. Notion of game theory can be used in intrusion detection system in assisting in defining and reconfiguring security policies given the severity of attacks dynamically. We are trying to formulate a robust model for the theoretical limits of a game theoretic approach to IDS. The most important flaw of game theory is that it assumes the adversary’s rationality and doesn’t take into consideration multiple simultaneous attacks. Therefore, a collaborative trust and Dirichlet distribution based robust game theoretic approach is proposed which will try to resolve this issue. Reinforced learning approaches using Markov Decision Process will be utilized to make it robust to multiple simultaneous attacks.