Reputation prediction in mobile ad hoc networks using RBF neural networks

Abstract

Security is one of the major challenges in the design and implementation of protocols for mobile ad hoc networks (MANETs). 'Cooperation for corporate well-being' is one of the major principles being followed in current research to formulate various security protocols. In such systems, nodes establish trust-based interactions based on their reputation which is determined by node activities in the past. In this paper we propose the use of a Radial Basis Function-Neural Network (RBF-NN) to estimate the reputation of nodes based on their internal attributes as opposed to their observed activity, e.g., packet traffic. This technique is conducive to prediction of the reputation of a node before it portrays any activities, for example, malicious activities that could be potentially predicted before they actually begin. This renders the technique favorable for application in trust-based MANET defense systems to enhance their performance. In this work we were able to achieve an average prediction performance of approximately 91% using an RBF-NN to predict the reputation of the nodes in the MANET. © 2009 Springer-Verlag.