Seeing is Believing: Detecting the Sybil Attacks in FANETs by Mapping Visual and Auditory Domains

As the derivative of the vehicular ad hoc network, the flying ad hoc network (FANET) will play a crucial role in the B5G/6G era since it provides wide coverage and on-demand deployment services in a distributed manner. The detection of Sybil attacks is essential to ensure trusted communication in FANET. Nevertheless, the conventional methods only utilize the untrusted information that UAV nodes passively “heard” from the “auditory” domain (AD), resulting in severe communication disruptions and even collision accidents. In this paper, we present a novel VA-mapping solution that matches the neighbors observed from AD and “visual” domain (VD), which is the first solution that enables UAVs to accurately correlate what they “see” from and “hear” to detect the Sybil attacks. The overhead in AD is reduced by the proposed topology-aware beacon interval. Relative entropy is utilized to describe the similarity of observed characteristics from dual domains. The dynamic weight algorithm is proposed to distinguish neighbors according to the characteristics’ popularity. The matching model of AD and VD is established and solved by the proposed vampire bat optimizer. Experiment results show that the proposed VA-mapping solution removes the unreliability of individual characteristics and single domains. It reduces the overhead without sacrificing the accuracy of the neighbor list. Furthermore, it outperforms the conventional RSSI-based and mobility-based methods in Sybil detection.