A Machine Learning Based Reputation System for Defending Against Malicious Node Behavior

Abstract

Reputation systems (RS) are designed to detect malicious nodes in a network and thwart their attacks, such as the spreading of viruses or worms, or attacking known vulnerabilities. They do this by collecting information about past transactions of a node and utilizing that to predict its future behavior. Traditionally, RSs have been designed by manually devising specific models or equations that use historical data to defend against certain types of attacks. In this paper, we propose a machine learning based RS that automates the process of devising the RS model and defends against many patterns of attacks. We discuss the merits of this approach and propose using support vector machines as the basis of the RS. We delineated the factors associated with building the SVM based RS and then proposed and evaluated our technique. We compared the performance of our RS with another RS found in the literature, called TrustGuard, and showed that our RS significantly outperforms TrustGuard. Our RS correctly distinguishes between good and malicious nodes with high accuracy, even when the proportion of malicious nodes in the network is very high.