A Reinforcement Learning Approach to Enhance the Trust Level of MANETs

A Mobile ad-hoc network (MANET) consists of many freely interconnected and autonomous nodes which are often composed of mobile devices. MANETs are decentralized and self-organized wireless communication systems which are able to arrange themselves in various ways and have no fixed infrastructure. Since MANETs are mobile, the network topology changes rapidly and unpredictably. Because of this nature of the mobility of the nodes in MANETs, the main problems that occur are, unreliable communications and weak security where the data can be compromised or easily misused. Therefore, we propose a trust enhancement approach to a MANET, which is based on RLTM (Reinforcement Learning Trust Manager), a set of algorithms that considers Ad-hoc On-demand Distance Vector (AODV) protocol as the specific protocol, via Reinforcement Learning (RL) and Deep Learning concepts. The proposed system consists of an RL agent that, learns to detect and give predictions on trustworthy nodes, reputed nodes, and malicious nodes and to classify them. The identified parameters from AODV simulation using Network Simulator-3(NS-3) were given to the designed RNN (Recurrent Neural Network) model and results were evaluated.