Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm - Maleknasab Ardakani, Mahdi; Tabarzad, Mohammad Ali; Shayegan, Mohammad Amin (The Journal of supercomputing, 2022)
In vehicular ad hoc networks (VANETs), Sybil attacks are serious security problems that can seriously affect the operations of the VANETs by producing fake identities and routes. To deal with this problem, in this paper, we present a cross-layer approach and fuzzy logic-based solution that should be applied in roadside units (RSUs). This scheme applies two fuzzy logic controllers (FLCs), in which the first one considers factors such as vehicles trust, Received signal strength indication (RSSI) difference, vehicle distance, and vehicle angle while the second one utilizes factors such as signal-to-noise ratio, network entry time, number of the neighbors, and buffer size. In addition, the arithmetic optimization algorithm (AOA) is applied for tuning the applied fuzzy sets and selecting the best possible rules in the proposed FLCs to improve their performance. Extensive simulation results were conducted using the NS2 and SUMO simulators for two scenarios, which the first one is allocated for in-town and the other is considered for out-town environments. The achieved results indicate that the proposed Sybil attack detection scheme outperforms other approaches in terms of the various metrics such as false positive rate (FPR), the number of dropped packets, and packet loss ratio are used.