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.
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Ham, F.M., Imana, E.Y., Ondi, A., Ford, R., Allen, W., Reedy, M. (2009). Reputation Prediction in Mobile Ad Hoc Networks Using RBF Neural Networks. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_46
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DOI: https://doi.org/10.1007/978-3-642-03969-0_46
Publisher Name: Springer, Berlin, Heidelberg
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