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The flying ad hoc network (FANET) will play a crucial role in the B5G/6G erasince it provides wide coverage and on-demand deployment services in adistributed manner. The detection of Sybil attacks is essential to ensuretrusted communication in FANET. Nevertheless, the conventional methods onlyutilize the untrusted information that UAV nodes passively ``heard'' from the``auditory" domain (AD), resulting in severe communication disruptions and evencollision accidents. In this paper, we present a novel VA-matching solutionthat matches the neighbors observed from both the AD and the ``visual'' domain(VD), which is the first solution that enables UAVs to accurately correlatewhat they ``see'' from VD and ``hear'' from AD to detect the Sybil attacks.Relative entropy is utilized to describe the similarity of observedcharacteristics from dual domains. The dynamic weight algorithm is proposed todistinguish neighbors according to the characteristics' popularity. Thematching model of neighbors observed from AD and VD is established and solvedby the vampire bat optimizer. Experiment results show that the proposedVA-matching solution removes the unreliability of individual characteristicsand single domains. It significantly outperforms the conventional RSSI-basedmethod in detecting Sybil attacks. Furthermore, it has strong robustness andachieves high precision and recall rates.

Seeing is Believing: Detecting Sybil Attack in FANET by Matching Visual and Auditory Domains