Özet:

Abstract Vehicular ad hoc networks (VANETs) are being used and progressively promoted for traffic control and accident prevention. A malicious node can be manufactured to be several vehicle nodes in a Sybil attack. It is generally agreed that vehicular ad hoc networks (VANETs) must rely heavily on peer-to-peer correspondence and malignant data traffic with the ultimate goal of achieving execution goals. The Sybil attack undermines attacks in which the aggressor deceives different nodes by the same incorrect ID or copy ID of the clients aware of the nodes in the WSN. Sybil attacks are named after an anecdotal personality with issues of dissociative uniqueness. The web-based social networking is under Sybil’s attack, and it affects the entire system. By multiplying deceptive profiles using false characteristics, Sybil attacks are attacks against informal online organizations’ reputations. In online social networks, bogus profiles have become a continuous and evolving danger. The line between the physical and online worlds is getting obscured as organizations and individuals grasp social networks. So, distinguishing, countering, and containing counterfeit records on online networks is critical. We collected datasets and performed a simulation to detect Sybil attacks. A dataset, which contains 1048576 records, is selected. A big data issue is a large dataset, so it was divided into chunks. The partitioned datasets are 11, and for detecting a Sybil attack, each is simulated. For detecting Sybil attacks in the network, a methodology is proposed. Sybil attacks have been detected by checking the similarity of each node’s attributes to existing nodes.

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