Detecting bad-mouthing attacks on reputation systems using self-organizing maps

Abstract

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack, and it can significantly deteriorate the performances of the network. The existing solutions for coping with bad mouthing are mainly concentrated on prevention techniques. In this work we propose a solution that detects and isolates the above mentioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrates the capability of the approach in detecting bad mouthing attack in various scenarios. © 2011 Springer-Verlag.