Network behavior anomaly detection (NBAD) provides one approach to network security threat detection. It is a complementary technology to systems that detect security threats based on packet signatures. In this process, proposed new approach for handling segmentation that is taken neighboring data segments as random variables. Then implement the prediction variance detector, in order to determine the predictabilities and spatial analysis. To optimize the covariance matrix by Spearman’s rank correlation coefficient and compression techniques. This proposed approach detect a wide range of long-term anomalies efficiently. Network Behavior Anomaly Detection (NBAD) is the continuous monitoring of a network for unusual events or trends. NBAD is an integral part of network behavior analysis (NBA), which offers security in addition to that provided by traditional anti-threat applications such as firewalls, intrusion detection systems. The development of anomaly detection techniques suitable for WSNs is therefore regarded as an essential research area, which will enable WSNs to be much more secure and reliable. The algorithms developed for anomaly detection have to consider the inherent limitations of sensor networks in their design so that the energy consumption in sensor nodes is minimized and the lifetime of the network is maximized. The communication overhead by clustering the sensor measurements and merging clusters before sending a description of the clusters to the other nodes.