Enhancing the security of OLSR protocol using reinforcement learning

Mobile ad-hoc networks are used in various institutions such as the military, hospitals, and various businesses. Due to their dynamic mobile structure-free and self-adaptive nature, they are ideal to be used in emergency situations where the resources available are limited. The wireless range of the devices in the MANET is narrow. In order to communicate with the desired device often times it is necessary to use intermediate devices between the source and the destination. Therefore, it is important to secure sensitive information sent through intermediate devices. OLSR is a widely used MANET routing protocol. Although OLSR protocol has excelled in performance and reliability, it is rather poor in security. In this context, we attempt to improve the security of OLSR protocol with the aid of Q-Learning by selecting trustworthy nodes to forward messages. Behavior of the nodes is used to determine the trust of the nodes.