Wireless Sensor Network (WSN) is considered as distributed large scale systems utilized to monitor and acquire the huge complex information for effective data management and situation handling. Due to the complex nature and node were considered as highly resource constraint. Hence, it becomes mandatory to establish the light weight security solutions to the monitoring node to prevent from various security challenges. However, WSN need a unique, distinct, and persistent discovery method in addition to establishing the security protocols against multiple threats. Especially Sybil attacks poses a high challenging threat to the network and its information. In order to mitigate the Sybil attack and its attacker. It is considered to be significant to determine the launching behaviour of the attacker. Sybil attacker generates more than one identity in the network to launch a coordinated attack. Further attacker has capability in navigating among the nodes during the detection process and it promotes the lack of network accountability. In this paper, a novel Sybil detection approach is proposed to detect the Sybil identities of Sybil attackers on employing the unsupervised classification phenomena’s for collecting the Sybil nodes evolution on the proposed network topology. Simulation analysis of the proposed model proves that it is highly efficient in exploiting the Sybil Nodes in the network on compared to traditional approaches with high detection accuracy with less false positive rate and detection speed.

Node Replica attack, Wireless Sensor network, Unsupervised Clustering, Distributed systems, Discrete Hidden Markov Model