Resilient Consensus for Multi-Agent Systems in the Presence of Sybil Attacks

This paper investigates the problem of resilient consensus control for discrete-time linear multi-agent systems under Sybil attacks. We consider a node to be a Sybil node if it can generate a large number of false identities in the graph as a way of gaining disproportionate influence on the consensus performance of the network. Such attacks can easily invalidate existing resilient consensus algorithms that assume an upper bound on the number of malicious nodes in the network. To this end, we first built a new attack model based on the characteristics of the Sybil nodes. In addition, a quantized-data-based transmission scheme was developed for identifying and resisting Sybil nodes in the network. Then, an attack-resilient consensus algorithm was developed, where each normal node sends the quantitative data information with a specific label, which is generated by truncated normal distribution sampling to their neighbors. We give sufficient graphical conditions for attack models considering limited energy to ensure the consensus of linear multi-agent systems. Finally, numerical simulation examples are provided to validate the effectiveness of the proposed methods.