Collusion node detection method based on fuzzy evaluation density clustering in Internet of vehicles - Haibo ZHANG; Dabin WANG; Ruyan WANG; Dongyu WANG (Tongxin xuebao)
In order to solve the security problems caused by collusion of malicious vehicles in the open Internet of vehicles environment, a collusion node detection method based on fuzzy evaluation density clustering was designed.The method realized vehicle reputation update through subjective recommendation trust, objective data trust and historical reputation.Two rounds malicious node detection method was designed to detect malicious vehicles, in the first round, fuzzy comprehensive evaluation was used to screen out a single malicious vehicle node, and in the second round, the conspiracy malicious vehicle nodes were searched by the improved density clustering method according to a single malicious vehicle node to ensure the sustainable development of network environment security.Experimental results show that the proposed method has a high recognition rate for malicious vehicles, and when the proportion of malicious vehicles reaches 30%, the detection accuracy can still remain above 90%.At the same time, under the proportion of different malicious vehicles, the detection recall rate and detection F value still remain higher value, indicating that the proposed method has a high stability.For the change of total vehicle nodes, the performance evaluation index shows a small fluctuation and remain within the range from 80% to 100%.