WSN has come as a useful technology and has proved to be a motivational force for new technology that is Internet of Things (IoT). Due to its characteristics limitations, security is one of key challenge for the performance of Wireless sensor networks. Blackhole attack is a major threat to WSN in which the malicious node holds all the data that is actually meant to transfer to the Base Station. In this paper a new technique is proposed to detect and prevent the Blackhole attack in WSN. The usage of the proposed technique prevents the deterioration in the performance of Wireless Sensor Network. This research paper presents an investigation into the anomaly-based detection of black hole attacks in WSNs. The objective is to develop a reliable and effective mechanism for identifying deviations from normal network behavior that may indicate the presence of black hole attackers.The proposed approach involves establishing a baseline of expected network behavior through profiling and monitoring various network parameters. Real-time monitoring and analysis are conducted to detect anomalies, considering statistical methods, machine learning algorithms, or rule-based techniques. These anomalies are indicative of potential black hole attacks within the network.Future research directions are also discussed, including the incorporation of advanced machine learning techniques, context-aware detection, real-time adaptation, and energy-efficient considerations.This research paper contributes to the field of wireless sensor network security by addressing the need for effective detection mechanisms against black hole attacks. The proposed anomaly-based approach enhances the overall resilience and security of WSN.