On the Impact of Witness-Based Collusion in Agent Societies
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Optimal trust mining and computing on keyed mapreduce
ESSoS'12 Proceedings of the 4th international conference on Engineering Secure Software and Systems
DART: A Distributed Analysis Of Reputation And Trust Framework
Computational Intelligence
| Hi-index | 0.00 |
Autonomous agents require trust and reputation concepts in order to identify communities of agents with which to interact reliably in ways analogous to humans. This paper defines a class of attacks called witness-based collusion attacks designed to exploit trust and reputation models. Empirical results demonstrate that unidimensional trust models are vulnerable to witness-based collusion attacks while independent multidimensional trust models are not. The paper demonstrates that here is a need for witness interaction trust to detect colluding agents in addition to the need for direct interaction trust to detect malicious agents. By proposing a set of policies, the paper demonstrates how learning agents can decrease the level of encounter risk in a witness-based collusive society.